Modeling and Simulation Resources

Modeling & Simulation Resources

The enormous potential of digital computation to manage new complex systems is impeded by exponential increases in complexity. The model's dimensionality increases from hundreds to thousands of variables, therefore, it is necessary have sub-models constructed by diverse technical teams to be integrated into the total computer simulation model. This site presents access to the recent advances in computer simulation for decision making that is of interest to researchers and graduate students across a number of academic domains.
I always welcome information regarding any further references for inclusion. You may like to contact me by sending me an email your comments/suggestions or corrections for improvement. Thank you.
Dr. Hossein Arsham

To search the site, try Edit | Find in page [Ctrl + f]. Enter a word or phrase in the dialogue box, e.g. "optimization" or "sensitivity" If the first appearance of the word/phrase is not what you are looking for, try Find Next.
MENU
General Resources
Interesting and Useful Sites (topical category)
Archival Journal Articles: Authors' Index
Journal Web Sites
Societies & Organizations
Books: Authors' Index
Additional Books and Journal Articles: Authors' Index

Companion Sites:

A Collection of JavaScript E-labs
A Collection of Financial Keywords and Phrases
A Collection of Keywords and Phrases for Economics
A Collection of Keywords and Phrases for Decision Making
A Compendium of External Web Sites Review
A Searchable List of All My Web Sites
My Online Books’ Reviews

Archival Journal Articles: Authors' Index

A B C D E F G H I

J K L M N O P Q R

S T U V W X Y Z

Abstract of the papers may be found at: MATH

General Resources
A Basic Scientific Calculator
A Catalog of Mathematics Resources on WWW and the Internet
A Collection of JavaScript E-labs
Agent-Based Computational Economics (ACE)
Applied Management Science
Bibliographies (Mathematics at Florida State University)
Bibliography (by Pierre L'Ecuyer)
BUBL Link
Collection of Computer Science Bibliographies
Computational Statistics
Computer Science Journals
Conferences |I |II |III |Conferences-Euresco|
Decision Support Systems Rresources (by Dan Power)
Directory of Computing Science Journals
DMSO: Defense Modeling and Simulation Office
Dynamical Systems Group
Engineering Virtual Library
Environmental Dynamics SIG
Excel For Statistical Analysis
Genetic Algorithms in Java
Genetic Algorithms Laboratory (Illinois)
Glossary of Modelling and Simulation
Goodness-of-Fit Test for Discrete Random Variables
IEEE Working Group on Discrete Event Systems
Index to Math Subject Classification
INFORMS College on Simulation
Institute of Applied Computer Science and Information Systems
Mathematics Archive
Mean, Standard Deviation, & Coefficient of Variation
M/M/1 Solver & Simulator (by Jarek Sklenar)
Model Benders (Roger Smith)
Modeling and Simulation
MultiSimplex Experimental Design & Optimization Software
National Academic Mailing List Service
National Simulation Resource
News: ARGE Simulation
Numerical Methods
On-line CS Techreports
Other Bibliographies on Mathematics
Performance Evaluation of Computers and Communication Networks
Petri Nets
Physical Sciences Information Gateway
PhysInfo (by Eddy Kestemont)
Priority Queues (by Lee Killough)
Probabilistic Modeling
Programación Estocastica (by Ramon Sala-Garrido)
Publishers-Archive
Publishers Around the World
Publishers-Math
Publishers (search)
Random Number Generators
Random Numbers and Monte Carlo Methods
Random Variates Generator
Robotics
Search the Network Bibliography
Sensitivity analysis (index)
Simulation (Math Forum)
Simulation Bookmarks (Parallel)
Simulation des systèmes stochastiques (by Felisa Vázquez-Abad)
Simulation Methods Expert Group
Simulation Software Survey
Statistics
Social Systems (by J. Frolova, and V. Korobitsin)
Software|
(SPSA) Simultaneous Perturbation Stochastic Approximation
Stochastic Programming Bibliography (by Mally van der Vlerk)
Stochastic Programming Community
Subject Area Pages
Systems & Simulation Links
Test for Homogeneity
Test for Normality
Test for Randomness
Tests for Random Numbers
Topics in Statistical Data Analysis
Virtual Control Library
Winter Simulation Conference
Zero Saga & Confusions with Numbers

Interesting and Useful Sites (topical category)

General Resources
Probability and Statistics for Simulation
Monte Carlo in Action
Simulation Courses
Major Simulation Sites

General Resources
| All Topics Periodicals |Computer Science Bibliography |News Groups|

Probability and Statistics for Simulation
|A Basic Scientific Calculator |Chi-square Test for Crosstable Relationship |Goodness-of-Fit Test for Discrete Random Variables | Mean, Standard Deviation, & Coefficient of Variation |Multinomial Distributions: Expected Value, Variance, Standard Deviation, & Coefficient of Variation |MultiVariate Statistics: Mean, Variance, & Covariance |Test for Homogeneity |Test for Normality |Test for Randomness | Bayes' Revised Probability Applet | Introduction to Statistics | The Probability Web | Interactive Statistics Page|
|Test for Randomness |Confidence Intervals |ANOVA in Detail |Statistics, Statistical Computing, and Mathematics |Bibliography for Computational Probability and Statistics | Business Statistics |Topics in Statistical Data Analysis | SimStat | Statistical Calculators on Web | T-test on the Web | Use and Abuse of Statistics | World Wide Resources | Virtual Library |MathForum | Introduction to Statistics | A New View of Statistics|
| Statistics Homepage | Hyper Stat | Computer-based Learning Statistics | Statistics on Web |Testing the Mean |Testing the Variance: Is the Quality that Good? | The P-values |American Statistical Association

Monte Carlo in Action
|Simulations/Demos | Java Applets | Markov System Simulation | Let's Make a Deal | Small Sample Size Effect | Central Limit Theorem | Buffon's Needle | Monte-Carlo| | Random Numbers Generators | History of Monte Carlo | Monte Carlo Methods and Applications|

Simulation Courses
|Advanced Modeling and Simulation Techniques | Lecture Notes | Modeling and simulation-I | Management Simulations Inc.|

Major Simulation Sites
| McLeod Institute of Simulation Science | CAI Members | OpEMCSS graphical discrete event simulation library | SIMSCRIPT | Winter Simulation Conference| | ACM SIGSIM Simulation | The Society for Computer Simulation International | DoD Simulation office |Simulation Tools|
|Environmental SIG | Control Society |SCS European Council | ACM Trans. on Modelling and Computer Simulation| | Complex Systems | Numerical Analysis Page | Design Research|
| Statistical Software Providers |Laboratory of Cybernetics and Decision Support Systems |Imagine That, Inc. |PowerSim Co. | EXTEND Software | Computer Simulations for Research Design |Simulation Software Survey|

Societies & Organizations

National Societies:
American OR/MS society
Association for the Modelling and Simulation in Enterprises
Australian Society for OR
Brazil OR Society
British OR Society
Canadian OR Society
Danish OR Society
Dutch OR Society
European Modelling and Simulation Societies
French OR Society
German OR Society
Hungarian OR
Italian consortium
Italy OR Society
New Zealand OR Society
Nordic OR Society
Portuguese OR Society
Singapore OR Society
South African OR Society
Organizations:
ACM Digital Library
ACM SIGSIM Simulation
AgentLink
ARGE Simulation
CAI Members
Chance-Constrained & Stochastic Programming
Complex Systems
Community of Science
Control Society
Decision Sciences Institute (DSI)
DoD: Modeling and Simulation
Environmental SIG
EXTEND Software
German Scientific Computing
IEEE Working Group on Discrete Event Systems
Imagine That, Inc.
INFORMS Simulation
Institute of Industrial Engineers
International Society for the Systems Sciences (ISSS)
Laboratory of Cybernetics and Decision Support Systems
McLeod Institute of Simulation Science
NCSTRL Collection
Networked Computer Science Technical Reports Library
Performance Measurement Association
PowerSim Co.
SCS European Council
SCS: Society for Computer Simulation
SIGSIM
SIMSCRIPT
Social Systems Simulation
Society for Computer Simulation International (SCS)
System Dynamics Group (Italy)
System Dynamics Organization
System Dynamics Society (US)
UK Systems Society
Winter Simulation Organization

Journal WebSites
ACM Transactions on Modeling and Computer Simulation
Asia-Pacific Journal of Operational Research
Australian & New Zealand Journal of Statistics
Automatica
Building and Environment
Communications in Statistics: Simulation and Computation
Computational Management Science
Computing and Visualization in Science
Computer Modeling in Engineering & Science
Computer Physics Communications
Computers & Industrial Engineering
Computers and Operations Research
Control and Cybernetics
Decision Sciences Journal
Discrete and Continuous Dynamical Systems
Engineering with Computers
European Journal of Operational Research
Evolutionary Computation
IEEE Journal of Systems, Man and Cybernetics Parts A, and B
IIE Transactions
INFOR
INFORMS Journal on Computing
International Journal of Engineering Simulation
International Journal of Information Technology Decision Making (IT&DM)
International Journal of Modelling and Simulation
International Journal of Nonlinear Sciences and Numerical Simulation
International Journal of Simulation and Process Modelling
International Journal of Statistics and Systems
International Journal of Systems Science
International Transactions in Operational Research
Inverse Problems: An Institute of Physics Journal
Journal of Artificial Intelligence Research
Journal of Computer and System Sciences
Journal of Control and Dynamical Systems
Journal of Economic Dynamics and Control
Journal of Evolutionary Modeling and Economic Dynamics
Journal of Interdisciplinary Mathematics
Journal of Mathematical Systems, Estimation, and Control
Journal of Process Control
Journal of Statistical Computation and Simulation
Journal of the ACM
Journal of Theoretical Probability
Linear Algebra and Its Applications
Management Science
Mathematical and Computer Modelling
Mathematics & Computers in Simulation
Mathematical Programming
Mathematics of Control, Signals and Systems
Microelectronics and Reliability
Monte Carlo Methods and Applications
Naval Research Logistics
Neural Computation
Operations Research Letters
Performance Evaluation
Probability Theory and Related Fields
Reliability Engineering & System Safety
Reliable Computing
Simulation & Gaming: An International Journal of Theory, Practice, and Research
Simulation Practice and Theory
Statistics & Probability Letters
Systems and Control Letters
Theory of Probability and its Applications

Journal Articles

Abate J., and W. Whitt, Transient behavior of regular Brownian motion, I and II, Advance Applied Probability 19, 560-631, 1987.
Abramson D., Constructing school timetables using simulated annealing: Sequential and parallel algorithms, Management Science, 37, 1991, 98-113.
Abspoel S, L. Etman, J. Vervoort, R. van Rooij, A. Schoofs, and J. Rooda, Simulation based optimization of stochastic systems with integer design variables by sequential multipoint linear approximation, Structural and Multidisciplinary Optimization, 22, 125-139, 2001.
Agnetis A., et al., Scheduling of flexible flow lines in an automobile assembly plant, Eur. J. Operational Research, 97, 1997, 348-362.
Ahmed S., Seasonal models of peak electric load demand, Technological Forecasting and Social Change, 72(5), 2005, 609-622.
Ahmed M., T. Alkhamis, and M. Hasan, Optimizing discrete stochastic systems using simulated annealing and simulation, Computers and Industrial Engineering, 32, 823-836, 1997.
Ahmed M., T. Alkhamis, D. Miller, Discrete search methods for optimizing stochastic systems, Computers & Industrial Engineering, 34, 703-716, 1998.
Ahn J-H., and J. Kim, Action-timing problem with sequential Bayesian belief revision process, Eur. J. Operational Research, 105, 1998, 118-129.
Akbay K., Using Simulation Optimization to Find the Best Solution, IIE Transactions, May 1996, 24-29.
Akmedjanov F., and S. Chelyshev, Robust stability investigation using frequency domain technique, Reliable Computing, 2 supplement, 9-10, 1996.
Alberto, I. C. Azcárate, F. Mallor, and P. Mateo, Optimization with simulation and multiobjective analysis in industrial decision-making: A case study, Journal of Operational Research, 140, 373-383, 2002.
Aleksandrov V., V. Sysoyeve, and V. Shemeneva, Stochastic optimization, Eng. Cybern., 5, 1968, 11-16.
Alessandri A. and T. Parisini, Nonlinear modelling of complex large-scale plants using neural networks and stochastic approximation, IEEE Transactions on Systems, Man, and Cybernetics: A, 27, 750-757, 1997.
Alexopoulos Ch., and A. Seila, A conservative method for selecting the best simulated system, Operations Research Letters, 19, 1996, 143-150.
Alkhamis T., Simulated annealing for discrete optimization with estimation, European Journal of Operational Research, 116, 530-544, 1999.
Al-Mharmah H., and J. Calvin, Optimal random non-adaptive algorithm for optimization of Brownian motion, Journal of Global Optimization, 8, 81-90, 1996.
Al-Qaq W., M. Devetsikiotis, and J. Townsen, Stochastic gradient optimization of importance sampling for the efficient simulation of digital communication systems, IEEE Transactions on Communications, 43, 2975-2985, 1995.
Alrefaei M., and S. Andradottir, A modification of the stochastic ruler method for discrete stochastic optimization, European Journal of Operational Research, 133, 160-182, 2001.
Al-Sultan K., A tabu search Hooke and Jeeves algorithm for unconstrained optimization, Eurp. J. Operational Research, 103, 1997, 198-208.
Andradóttir S., A global search method for discrete stochastic optimization, SIAM Journal on Optimization, 6, 513-530, 1996.
Andradóttir S., Optimization of the transient and steady-state behavior of discrete event systems, Management Science, 42, 717-737, 1996.
Andradóttir S., A stochastic approximation algorithm with varying bounds, Operations Research, 43, 1995, 1037-1048.
Andradóttir S., A scaled stochastic approximation algorithm, Management Science, 42, 475-498, 1996.
Andradóttir S., Optimization of transient and steady-state behavior of discrete event systems, Management Science, 42, 717-737, 1996.
Andradóttir S., A method for discrete stochastic optimization, Management Science, 41, 1946-1961, 1995.
Andradóttir S., D. Heyman, and T. Ott, On the choice of alternative measures in importance sampling with Markov chains, Operations Research, 33, 1995, 509-519.
Andramonov M., A. Rubinov, and B. Glover, Cutting angle methods in global optimization, Applied Mathematics Letters, 12, 95-100, 1999.
Andres T., Sampling methods and sensitivity analysis for large parameter sets, Journal of Statistics Computation and Simulation, 57, 77-110, 1997.
Apeland S., and T. Aven, Risk based maintainance optimaization: Foundational issues, Reliability Engineering and System Safety, 67, 285-292, 2000.
Araki Y. and K. Inoue, Comparison of the extremal search method by human being and machine, System and Control, 20, 106-115, 1976.
Archetti F., A. Gaivoronski, and A. Sciomachen A., Sensitivity analysis and optimization of stochastic petri nets, Discrete Event Dynamic System: Theory and Applications, 3, 5-37, 1993.
Arinze B., and J. Burton, A simulation model for industrial marketing, Omega, 20(3), 1992, 323-335.
Armstrong J., R. Black, D. Laxton, and D. Rose, A robust method for simulating forward-looking models, Journal of Economic Dynamics and Control, 22, 489-501, 1998.
Arsham H., Gradient-Based optimization techniques for discrete event systems simulation,
The Wiley Encyclopedia of Computer Science and Engineering, John Wiley & Sons, Vol.(3 of 5), 1429-1446, 2009.
Arsham H., Monte Carlo techniques for parametric finite multidimensional integral equations, Monte Carlo Methods and Applications, 13, 173-195, 2007.
Arsham H., The use of simulation in discrete event dynamic systems design, Journal of Systems Science, 31, 563-573, 2000.
Arsham H., Input parameters to achieve target performance in stochastic systems: A simulation-based approach, Inverse Problems in Engineering, 7, 363-384, 1999.
Arsham H., Techniques for Monte Carlo optimizing, Monte Carlo Methods and Applications, 4, 181-230, 1998.
Arsham H., Algorithms for sensitivity information in discrete-event systems simulation, Simulation Practice and Theory, 6, 1-22, 1998.
Arsham H., Goal seeking problem in discrete event systems simulation, Microelectronics and Reliability, 37, 391-395, 1997.
Arsham H., A test sensitive to extreme hidden periodicities, Stochastic Hydrology and Hydraulics, 11, 323-330, 1997.
Arsham H., Performance extrapolation in discrete-event systems simulation, International Journal of Systems Science, 27, 863-869, 1996.
Arsham H., Stochastic optimization of discrete event systems simulation, Microelectronics and Reliability, 36, 1357-1368, 1996.
Arsham H., A solution algorithm for stochastic equations arising from discrete- event systems simulations, In Modelling and Simulation, Instrument Society of America, 23, 1815-1822, 1992.
Arsham H., A simulation technique for estimation in perturbed stochastic activity networks, Simulation, 58, 258-267, 1992.
Arsham H., Perturbation analysis in discrete-event simulation, International Journal of Modelling & Simulation, 11, 21-28, 1991.
Arsham H., What-if analysis in computer simulation models: A comparative survey with some extensions, Mathematical and Computer Modelling, 13, 101-106, 1990.
Arsham H., On the inverse problem in Monte-Carlo experiments, Inverse Problems, 5, 927-934, 1989.
Arsham H., Sensitivity and optimization of computer simulation models, Modeling and Simulation, Instrument Society of America, 19, 1835-1842, 1988.
Arsham H., Simulation based decision support for systems design and control, Organization (Organizacija): Journal of Management, Information Systems and Human Resource, 39, 626-634, 2006.
Arsham H., Feuerverger, A., McLeish, D., Kreimer J. and Rubinstein R., Sensitivity analysis and the what-if problem in simulation analysis, Mathematical and Computer Modelling, 12, 193-219, 1989.
PDF Version
Asmussen S., and R. Rubinstein, The efficiency and heavy traffic properties of the score function method in sensitivity analysis of queueing models, Advances in Applied Probability, 24, 172-201, 1992.
Asmussen S., and R. Rubinstein, Response surface estimation and sensitivity analysis via the efficient change of measure, Comm. Stat. Stoch. Models, 9, 313-339, 1993.
Asmussen S., and C-L. Wang, Regenerative rare events simulation via likelihood ratios, Journal of Applied Probability, 31, 1994, 797-815.
Atienza O., and G. Hong, Computer simulation: An effective tool for teaching statistical optimization procedures, Quality Engineering, 10(3), 499, 1998.
Au G., and R. Paul, A graphical discrete event simulation environment, INFOR, 35, 121-137, 1997.
Aytug H., C. Dogan, and G. Bezmez, Determining the number of kanbans: A simulation metamodelling approach, Simulation, 67, 23-32, 1996.
Aytug H., S. Bhattacharyya, and G. Koehler, Genetic learning through simulation: An investigation in shop floor scheduling, Annals of Operations Research, 78, 1-29, 1998.
Azadivar F. and Lee Y-H., Optimization of discrete variable stochastic systems by simulation, Mathematics and Computer in Simulation, 30, 1988, 331-345.
Azadivar F., and J. Talavage, Optimization of stochastic simulation models, Mathematics and Computers in Simulation, 22, 231-241, 1980.
Azadivar F., G. Tompkins, Simulation optimization with qualitative variables and structural model changes: A genetic algorithm approach, European Journal Of Operational Research, 113, 1999, 169-182.

Bäck T., and H. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, 1, 1-23, 1993.
Badiru A., Neural network as a simulation metamodel in economic analysis of risky projects, European Journal of Operational Research, 105, 1998, 130-142.
Badiru A., and D. Sieger, Neural network as a simulation metamodel in economic analysis of risky projects, Eur. J. Operational Research, 105, 1998, 130- 142.
Baines T., S. Masona, P-O. Siebersa, and J. Ladbrookb, Humans: the missing link in manufacturing simulation?, Simulation Modelling Practice and Theory, 12(7-8), 2004, 515-526
Bandyopadhyaya S., J. Reesb, and J. Barron, Simulating sellers in online exchanges, Decision Support Systems, 41(2), 2006, 500-513.
Balci O., (Editor), Simulation and Modeling, Annals of Operations Research, 53, 1994.
Balintfy J., and L. Lancaster, Simulation analysis of school lunch planning policies, Socio-Economic Planning Sciences, 32, 1998, 87-97.
Bao G., C. Cassandras and M. Zazanis, First and second derivative estimators for cyclic closed queueing networks, IEEE Trans. on Automatic Control, 41, 1210-1213, 1996.
Barron E., P. Cardaliaguet, and R. Jensen, Radon - Nikodym Theorem in L_infinity, Applied Mathematics & Optimization, 42, 103-126, 2000.
Barton R., and J. Ivey, Jr., Nelder-Mead simplex modifications for simulation optimization, Management Science, 42, 1996, 954-973.
Batmaz I., and S. Tunali, Small response surface designs for metamodel estimation, European Journal of Operational Research, 145, 455-470, 2003.
Beckman R., and M. McKay, Monte Carlo estimation under different distributions using the same simulation, Technometrics, 29, 1987, 153-160.
Bedoni M., Strategies simulation in an aggregate bank model, European Journal of Operational Research, 30, 1987, 24 -29
Bekey G., and M. Ung, A comparative evaluation of two global search algorithms, IEEE Trans. on SMC, 4, 112-118, 1974.
Bélisle C., Convergence theorems for a class of simulated annealing algorithms on R^d, Journal of Applied Probability, 29, 1992, 885-895.
Benson D., Simulation modeling and optimization using ProModel, in the Proceedings of the Winter Simulation conference, 1996.
Berends P., and G. Romme, Cyclicality of capital-intensive industries: A system dynamics simulation study of the paper industry, Omega, 29, 543-552, 2001.
Betro B., Bayesian methods in global optimization, Journal of Global Optimization, 1, 1-14, 1991.
Bettonvil B., A formal description of discrete event dynamic systems including infinitesimal perturbation analysis, European Journal of Operational Research, 42, 213-222, 1989.
Bettonvil B., J. Kleijnen, Searching for important factors in simulation models with many factors: Sequential bifurcation, Eur. J. Operational Research, 96, 1997, 180-194.
Beyn W-J, and W. Kless, Numerical Taylor expansions of invariant manifolds in large dynamical systems, Numerische Mathematik, 80, 1998, 1-38
Bhaté-Felsheim A., et. al., Simulation of a probation/parole system, Socio-economic Planning Sciences, 36, 139-154, 2002.
Biester Ch., P. Grabner, G. Larcher, and R. Tichy, Adaptive search in quasi-Monte Carlo optimization, Math. Comp., 64, 1995, 807-818.
Biethhan J., and V. Nissen, Combinations of simulation and evolutionary algorithms in management science and economics, Annals of Operations Research, 52, 1994, 183-208.
Birge J, and F. Louveaux, Introduction to Stochastic Programming, Springer, New York, 1997.
Borkar V., Asynchronous stochastic approximations, SIAM Journal on Control and Optimization, 36(3), 1998.
Borovkov K., On simulation of random vectors with given densities in regions and on their boundaries, Journal of Applied Probability, 31, 1994, 205--220.
Bosch P, and A. Klauw, Modeling, Identification and Simulation of Dynamical Systems, CRC Press, 1994.
Bowman R., Stochastic gradient-based time-cost tradeoffs in PERT network using simulation, Annals of Operations Research, 53, 533-551, 1994.
Brailsford S., and Bernd Schmidt, Towards incorporating human behaviour in models of health care systems: An approach using discrete event simulation, European Journal of Operational Research, 150, 19-31, 2003.
Brémaud P., Maximal coupling and rare perturbation sensitivity analysis, Queueing Systems: Theory and Applications, 10, 1992, 249-270.
Brémaud, P. and F. Vázquez-Abad, On the pathwise computation of derivatives with respect to the rate of a point process: The phantom RPA method, Queueing Systems, 10, 1992, 249-270.
Brennan R., and P. Rogers, Stochastic optimization applied to a manufacturing system operation problem, in the Proceedings of the Winter Simulation conference, 1995.
Brooks D. and W. Verdini, Computational experience with generalized simulated annealing over continuous variables, Am. J. Math. Manage. Sci., 8, 1988, 425-449.
Bucha C., J. Doepkeb, and Chr. Pierdzioch, Financial openness and business cycle volatility, Journal of International Money and Finance, 24(5), 2005, 744-765.
Butler J., Simulation techniques for the sensitivity analysis of multi-criteria decision models, European Journal of Operational Research, 103, 1998, 531-546.

Cantoni M, M. Marseguerra, and E. Zio, Genetic algorithms and Monte Carlo simulation for optimal plant design, Reliability Engineering and System Safety, 68, 29-365, 2000.
Caflisch R., Monte Carlo and quasi-Monte Carlo methods, Acta Numerica, 7, 1998, 1-50.
Cao X-R., Perturbation analysis of discrete event systems: Concepts, algorithms, and applications, European Journal of Operational Research, 91, 1-13, 1996.
Cao X-R., Performance sensitivity analysis of open Markovian queueing networks, Eur. J. Operational Research, 76, 1994, 529-551
Cao X-R., Realization probability in multi-class closed queueing networks, European Journal of Operational Research, 36, 393-401, 1988.
Cao X-R., Realization probability in closed Jackson queueing networks and its application, Adv. in Appl. Prob., 19, 708-738, 1987.
Cao X-R., Sensitivity estimates based on one realization of stochastic system, Journal of Statistical Computation and Simulation, 27, 211-232, 1987.
Cao X-R., Convergence of parameter sensitivity estimates in a stochastic experiment, IEEE Trans. Autom. Control, AC-30, 845-853, 1985.
Cao Q., W. Patterson, and X. Bai, Reexamination of processing time uncertainty, European Journal of Operational Research, 164(1), 2005, 185-194.
Caramanis M., and G. Liberopoulos, Perturbation analysis for the design of flexible manufacturing system flow controllers, Operations Research, 40, 1992, 1107-1125.
Carcano G., P. Falbo, and S. Stefani, Speculative trading in mean reverting markets, European Journal of Operational Research, 163(1), 2005, 132-144.
Cario M., and B. Nelson, Autoregressive to anything: Time-series input processes for simulation, Operations Research Letters, 19, 51-58, 1996.
Carmone Jr. F., A Monte Carlo investigation of incomplete pairwise comparison matrices in AHP, Eurp. J. Operational Research, 103, 1997, 538-553.
Carson T., Optimization and evaluation, in the Proceedings of the Winter Simulation conference, 1996.
Carson T., and A. Maria, Simulation optimization: Methods and Applications, in the Proceedings of the Winter Simulation conference, 118-126, 1997.
Caruso C., and F. Quarta, Interpolation methods comparison, Computers and Mathematics with Applications, 35, 1998, 109-126.
Cassandras C., and S. Strickland, On-line sensitivity analysis of Markov chains, IEEE Transactions on Automatic Control, 34, 1989, 76-86.
Castellacci G., and M. Siclari, The practice of Delta–Gamma VaR: Implementing the quadratic portfolio model, European Journal of Operational Research, 150(3), 2003, 529-545.
Catoni O., Rough large deviation estimates for simulated annealing: Application to exponential schedules, Annals of Probability, 20, 1992, 1109-1146.
Cellier F., How to enhance the robustness of simulation software, Systems Analysis, Modelling and Simulation, 1, 55-61, 1984.
Ceric V. and L. Lakatos, Measurement and analysis of input data for queueing systems models used in system design, System Analysis Modelling Simulation, 11, 227-232, 1993.
Chan K., S. Tarantola, and A. Saltelli, Sensitivity analysis of model output: Variance-based methods make the difference, in the Proceedings of the Winter Simulation Conference, 261-268, 1997.
Chaturvedia A., S. Mehtaa, D. Dolkb, and R. Ayerc, Agent-based simulation for computational experimentation: Developing an artificial labor market, European Journal of Operational Research, 166(3), 2005, 694-716.
Chelouah R., and P Siarry, Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions, European Journal of Operational Research, 148, 335-348, 2003.
Chen C-H., A lower bound for the correct subsetselection probability and its application to discrete event systems simulations, IEEE Transactions on Automatic Control, 41, 1227-1231, 1996.
Chen C-H., K. Donohue, E. Yucesan, and J. Lin, Optimal computing budget allocation for Monte Carlo simulation with application to product design, Simulation Modelling Practice and Theory, 11, 57-74, 2003.
Chen F., and Y-Sh. Zheng, Sensitivity analysis of an (s,S) inventory model, Operations Research Letters, 21, 1997, 19-23.
Chen H-C., C-H. Chen, L. Dai, and E. Yucesan, New development of optimal computing budget allocation for discrete event simulation, in the Proceedings of the Winter Simulation Conference, 334-341, 1997
Chen H-F., Convergence analysis of dynamic stochastic approximation, Systems & Control Letters, 35, 309-315, 1998.
Chen H-F., and Y.M. Zhu, Stochastic approximation procedures with randomly varying truncations, Scientia Sinica: Series A, 29, 1986, 914-926.
Chen H-F., T. Duncan, and B. Pasik-Duncan, A stochastic approximation algorithm with random differences, Proceedings of the 13^th IFAC World Congress, H, 493-496, 1996, (alternative convergence conditions for SPSA).
Chen M-H., and B. Schmeiser, Performance of the Gibbs, hit-and-run, and Metropolis samplers, Journal of Computational and Graphical Statistics 2, 1993, 251-272
Cheng R., Searching for important factors: Sequential bifurcation under uncertainty, in the Proceedings of the Winter Simulation Conference, 275-280, 1997.
Cheng R., and W.. Holland, Sensitivity of computer simulation experiments to errors in input data, Journal of Statistical Computation and Simulation, 57, 1997, 219-242.
Chen R-R., and S. Meyn, Value iteration and optimization of multiclass queueing networks, Queueing Systems, 32, 65-97, 1999.
Chiang T.-S. and Y. Chow, The asymptotic behavior of simulated annealing processes with absorption, SIAM Journal on Control and Optimization, 32, 1994, 1247-1265.
Chick S., Selecting the best system: A decision-theoretic approach, in the Proceedings of the Winter Simulation Conference, 326-333, 1997.
Chin D., Comparative study of stochastic gradient-free algorithms for system optimization, Proceedings of the American Control Conference, 3070-3075, 1994.
Chin D., Comparative study of stochastic algorithms for systems optimization based on gradient approximations, IEEE Transactions on Systems, Man, and Cybernetics-Part B, 27, 1997, 244-249. (theoretical and numerical efficiency analysis).
Chin D., A more efficient global optimization algorithm based on Styblinski and Tang, Neural Networks, 7, 1994, 573-574 (global optimization implementation).
Chin D., and R. Smith, R.H., A traffic simulation for Mid-Manhattan with model-free adaptive signal control, Proceedings of the Summer Computer Simulation Conference, 1994, 296-301 (traffic control application).
Chisman J., Using discrete simulation modeling to study large-scale system reliability/availability, Computers and Operation Research, 25, 169-174, 1998.
Cho S., A distributed time-driven simulation method for enabling real-time manufacturing shop floor control, Computers & Industrial Engineering, 49(4), 2005, 572-590
Choi D-H., Cooperative mutation based evolutionary programming for continuous function optimization, Operations Research Letters, 30, 195-201, 2002.
Chong E., Optimization of tandem networks using a distributed asynchronous algorithm with IPA stimulators, in Proc. 1992 American Control Conference, 3196-3200, 1992.
Chong E., On-line optimization of queues using infinitesimal perturbation analysis, in Discrete Event Systems, Manufacturing Systems, and Communication Networks, (Eds.) Kumar P, and P. Varaiya, IMA Volume 73, Springer-Verlag, 1993.
Chong E., and P. Ramadge, Convergence of recursive optimization algorithms using infinitesimal perturbation analysis estimates, Discrete Event Dynamic Systems: Theory and Applications, 1, 1992, 339-372.
Chong E., and P. Ramadge, Optimization of queues using an infinitesimal perturbation analysis-based stochastic algorithm with general update times, SIAM Journal on Control and Optimization, 31, 1993, 698-732.
Chong E., and P. Ramadge, Stochastic optimization of regenerative systems using infinitesimal perturbation analysis, IEEE Transactions on Automatic Control, 39, 1994, 1400-1410.
Chong E., and P. Ramadge, Optimal load sharing in soft real-time systems using likelihood ratios, Journal of Optimization Theory and Applications, 82, 1994, 23-48.
Choo E., and C. Kim, One dimensional simplex search, Computers and Operations Research, 14, 1987, 47-54.
Chwif L., Barretto, M., and L. Moscato, A solution to the facility layout problem using simulated annealing, Computers in Industry, 36, 125-132, 1998
Clark D., Necessary and sufficient conditions for the Robbins-Monro method, Stochastic Processes and Their Applications, 17, 359-367, 1984.
Clarkson K., Las Vegas algorithm for linear and integer programming when the dimension is small, Journal of association for Computer Machinery, 42, 488-499, 1995
Clymer J., System design and evaluation using discrete event simulation with AI, Eur. J. Operational Research, 84, 1995, 213-225.
Coates E., and M. Kuhl, Using simulation software to solve engineering economy problems, Computers & Industrial Engineering, 45(2), 2003, 285-294.
Cochran J., and J. Chang, Optimization of multivariate simulation output models using group screening method, Computers in Industrial Engineering, 18, 1990, 95-103.
Coit D., and A. Smith, Penalty guided genetic search for reliability design optimization, Computers and Industrial Engineering, 30, 1996, 895-904.
Coit D., and A. Smith, Reliability optimization of series-parallel systems using a genetic algorithm, IEEE Transactions on Reliability, 45, 1996, 254-260.
Connolly D., General purpose simulated annealing, Journal of Operational Research Society, 43, 495-505, 1992.
Cooley B. and E. Houck E., A variance reduction strategy for RSM simulation studies, Decision Sciences, 13, 303-321, 1982.
Consiglio A., and S. Zenios, Integrated simulation and optimization models for tracking international fixed income indices, Mathematical Programming, 89, 311-339, 2001.
Courrieu P., A distributed search algorithm for global optimization on numerical space, Recherche Operationnelle, 27, 327-335, 1993.
Coyle R., Simulation by repeated optimisation, Journal of the Operational Research Society, 50, 429-438, 1999.
Crawford J., and T. Gallwey, Bias and variance reduction in computer simulation studies, European Journal of Operational Research, 124, 571-590, 2000.
Crocker J., Effectiveness of maintenance, Journal-of-Quality-in-Maintenance-Engineering, 5, 307-13, 1999.
Crouch I., A. Greenwood, and L. Rees, Use of a classifier in a knowledge-based simulation optimization systems, Naval Research Logistic, 42, 1203-1232, 1995.
Curry G., and D. Hartfiel, A simulation optimization methods: its convergence and utility, Naval Research Logistics, 7, 31-41, 1983.

Dai L., Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems, Journal of Optimization Theory and Applications, 91, 363-388, 1996.
Damerdji H., Strong consistency and other properties of the spectral variance estimator, Management Science, 37, 1991, 1424-40.
Damerdji H., Maxium likelihood ratio estimation for generalized semi-Markov processes, Discrete Event Dynamic Systems: Theory and Applications, 6, 73- 104, 1996.
Damerdji H., and M. Nakayama, Two-stage multiple-comparison procedures for steady-state simulations, ACM Transactions on Modeling and Computer Simulations, 9, 1-30, 1999.
Dangelmaier W., M. Fischer, J. Gausemeier, M. Grafe, C. Matysczok, and B. Mueck, Virtual and augmented reality support for discrete manufacturing system simulation, Computers in Industry, 56(4), 2005, 371-383.
Daugherty A., and M. Turnquist, Budget constrained optimization of simulation models via estimation of their response surfaces, Operations Research, 29, 1981, 485-500.
Davies R., and P. Roderick, Planning resources for renal services throughout UK using simulation, Eur. J. Operational Research, 105, 1998, 285-295
Davies R., P. Roderick, and J. Raftery, The evaluation of disease prevention and treatment using simulation models, European Journal of Operational Research, 150, 53-66, 2003.
Deala B., and D. Schunk, Spatial dynamic modeling and urban land use transformation: a simulation approach to assessing the costs of urban sprawl, Ecological Economics, 51(1-2), 2004, 79-95.
De Angelis V., G. Felici, and P. Impelluso, Integrating simulation and optimisation in health care centre management, European Journal of Operational Research, 150, 101-114, 2003.
Dekker R., and P. Scarf, On the impact of optimising models in maintenance decision making: A state of the art, Reliability Engineering and System Safety, 60, 1998, 111-119.
Delyon B., and A. Juditsky, Acceleration stochastic approximation, SIAM Journal on Optimization, 3, 1993, 868-881.
Dempster M., Sequential importance sampling algorithms for dynamic stochastic programming, Annals of Operations Research, 84, 153-184, 1998.
Derek A., Performance evaluation of scheduling control of queueing networks: Fluid model heuristics, Queueing Systems, 21, 1996, 391-413
Desrochers A., and R. Al-Jaar, Applications of Petri Nets in Manufactureing Systems: Modeling, Control, and Performance Analysis, IEEE, 1994.
Dessouky Y., and A. Bayer, A simulation and design of experiments modeling approach to minimize building maintenance costs, Computers and Industrial Engineering, 43, 423-436, 2002.
Devetsikiotis M., and K. Townsend, Statistical optimization of dynamic importance sampling parameters for efficient simulation of communication networks, IEEE/ACM Trans. Network, 1, 293-305, 1993.
DeVol T., W. Moses, and S. Derenzo, Monte Carlo optimization of depth-of-interaction in PET crystals, IEEE Trans. Nucl. Sci., NS-40, 170-174, 1993.
Devroye L., On the convergence of statistical search, IEEE Transactions on SMC, 6, 46-56, 1976.
Dippon J., and J. Renz, Weighted means in stochastic approximation of minima, SIAM Journal of Control and Optimization, 35, 1811-1827, 1997.
Dolgui A., and D. Ofitserov, A stochastic method for discrete and continuous optimization in manufacturing systems, Journal of Intelligent Manufacturing, 8, 405-413, 1997.
Donohue J., E. Houck, and R. Myers R., Simulation design for the estimation of quadratic response surface gradients in the presence of model misspecification, Management Science, 41, 244-262, 1995.
Draganova C., Smoothest interpolation in the mean, Journal of Approximation Theory, 98, 223-247, 1999.
Dudewicz E., and Z. Karian, Tutorial: Modern Design and Analysis of Discrete-Event Computer Simulations, IEEE Computer Society Press, Los Angeles, CA, 1985.
Duenyas I., and M. Van Oyen, Stochastic scheduling of parallel queues with set-up costs, Queueing Systems, 19, 1995, 421-444.
Dupacova J., Stability and sensitivity-analysis for stochastic programming., Ann. Oper. Res., 27, 1990, 115-142.
Dupacova J., On statistical sensitivity analysis in stochastic programming, Annals of Operations Research, 30, 199-214, 1991.
Dupuis P., and R. Simha, On sampling controlled stochastic approximation, IEEE Trans. Auto. Control, AC36, 1991, 915-924.
Dussault J., D. Labrecque, P. L'Ecuyer, and R. Rubinstein, Combining the stochastic counterpart and stochastic approximation methods, Discrete Event Dynamic Systems: Theory and Applications, 7, 5-28, 1997.

Eglese R., Simulated annealing: A tool for operational research, European Journal of Operational Research, 40, 271-281, 1990.
Ermoliev Y., Stochastic quasigradient methods and their application to system optimization, Stochastics, 9, 1-36, 1983.
Ermoliev Y., and A. Gaivoronski, Stochastic programming techniques for optimization of discrete event systems, Ann. Oper. Res., 39, 1-41, 1992.
Ermoliev Y., and V. Norkin, Normalized convergence in stochastic optimization, Annals of Operations Research, 30, 187-198, 1991.
Ernst R., S. Powell, Optimal inventory policies under service-sensitive demand, Eur. J. Operational Research, 87, 1995, 316-327.

Fabian V., On asymptotic normality in stochastic approximately, Annals of Mathematical Statistics, 6, 1968, 1087-1094.
Farrell, W., Literature review and bibliography of simulation optimization, Proceedings of the Winter Simulation Conference, 117-124, 1977.
Farto J., An algorithm for the systematic construction of solutions to perturbed problems, Computer Physics Communications, 111, 110-132. 1997.
Feldman M., (Editor), Mathematical Evolutionary Theory, Princeton University Press, 1989.
Feo, T., and M. Resende, Greedy randomized adaptive search procedures, Journal of Global Optimization, 6, 1995, 109-133.
Fischer J., Some remarks on optimizing simulated systems, In System Analysis and Simulation: Theory and Foundation, Eds. Sydow A., Tzafesta S. and Vichnevesky R., 251-554, 1988.
Fleischer M., 1995, Simulated annealing: Past, present, and future, Proceedings of Winter Simulation Conference, 155-161.
Fogel D., A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems, Simulation, 64, 397-404, 1995.
Foster W., Expert system for industrial applications, Simulation, 34, 307-309, 1985.
Fowler A., Systems modelling, simulation, and the dynamics of strategy, Journal of Business Research, 56(2), 2003, 135-144.
Fox B., Simulated annealing: Folklore, facts, and directions, in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, Lecture Notes in Statistics, 106, 17-48, Springer, 1995.
Fox B., and G. Heine, Probabilistic search with override, Annals of Applied Probability, 5, 1995, 1087-1094.
Fraedrich D., and A. Goldberg, A methodological framework for the validation of predictive simulations, European Journal of Operational Research, 124, 55-62, 2000.
Frater M., T. Lennon, and B. Anderson, Optimally efficient estimation of the statistics of rare events in queueing networks, IEEE Trans. Automat. Contr., AC-36, 1395-1405, 1991
Frater M., J. Walrand, and B. Anderson, Optimality and efficient estimation of the buffer overflow in queues with deterministic service times, Australian Telecommun. Res., 24, 1-8, 1990.
Fu M., Optimization via simulation: A review, Annals of Operations Research, 35, 199-247, 1992.
Fu M., Convergence of a stochastic approximation algorithm for the GI/G/1 queue using infinitesimal perturbation analysis, Journal of Optimization Theory and Applications, 65, 1990, 149-160.
Fu M., and K. Healy K., Techniques for optimization via simulation: an experimental study on an (s, S) inventory system, IIE Transactions, 29, 191-199, 1997.
Fu M., and S. Hill, Optimization of discrete event systems via simultaneous perturbation stochastic approximation, IIE Transactions, 29, 233-243, 1997.
Fu M., and J-Q. Hu, Extensions and generalizations of smoothed perturbation analysis in a generalized semi-Markov process framework, IEEE Trans. Auto Control, AC-37, 1992, 1483-1500.
Fu M., and J-Q. Hu, On choosing the characterization for smoothed perturbation analysis, IEEE Trans. Auto Control, AC-36, 1991, 1331-1336.
Fu M., and J-Q. Hu, Sensitivity analysis for Monte Carlo simulation of option pricing, Probability in the Engineering and Information Sciences, 9, 417-449, 1995.
Fu M., and J-Q., Hu, Smoothed perturbation analysis for queues with finite buffers, Queueing Systems, 14, 1993, 57-58.
Fu M., and J-Q. Hu, Second derivative sample path estimators for the GI/G/m queue, Management Science, 39, 1993, 359-83.
Fu M., and J-Q. Hu, (s, S) iventory systems with random lead times: Harris recurrence and its implication in sensitivity analysis, Probability in the Engineering and Information Sciences, 8, 355-376, 1994.
Fu M., and X. Xie, Derivative estimation for buffer capacity of continuous transfer lines subject to operation-dependent failures, Discrete Event Dynamic Systems, 12, 447-469, 2002.
Fujimoto R., Parallel discrete event simulation, Commun. ACM, 33, 1990, 30-53.
Futschik A., and G. Pflug, Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms, European Journal of Operational Research, 101, 245-260, 1997.
Futschik A., and G. Pflug, Confidence sets for discrete stochastic optimization, Annals of Operations Research, 56, 95-108, 1995.

Gaither N., An experimental solution of the general stochastic programming problem, Simulation, 30, 1978, 191-195.
Gaivoronski A., Optimization of stochastic discrete event dynamic systems: A survey of some recent results, Simulation and optimization, Lect. Notes Econ. Math. Syst., Springer, 374, 1992, 24-44.
Gaivoronski A., Optimization of stochastic discrete event dynamic systems: a survey of some recent results, Lecture Notes in Economics and Mathematical Systems 374, Proc. Workshop on Simulation and Optimization, Laxenburg, Austria, ed. G. Pflung and U. Dieter, 1992, 24-44.
Gaivoronski A., L. Shi, and R. Sreenivas, Augmented infinitesimal perturbation analysis: An alternate explanation, Discrete Event Dynamic Systems: Theory and Applications, 2, 1992, 121- 138
Garcia-Diaz A., and F. Tari, Combining simulation and optimization to solve the multi-machine interference problem, Simulation, 36, 1981, 193-201.
Gassmann H., Modelling support for stochastic programs, Annals of Operations Research, 82, 1998, 107-138.
Gassmann H., Numerical techniques for stochastic optimization, Springer Ser. Comput. Math., 10, 1988, 237-254.
Gelfand S., and S. Mitter, Simulated annealing with noisy or imprecise energy measurements, Journal of Optimization Theory and Applications, 62, 1989, 49-62.
Gelfand S., and S. Mitter, Recursive stochastic algorithms for global optimization in R^d, SIAM, Journal on Control and Optimization, 29, 1991, 999-1018.
Gerencsér L., Rate of convergence of moments of Spall's SPSA method, Proceedings of the European Control Conference, 1997, (convergence conditions for means and other moments of SPSA iterate).
Gerencsér L., Convergence rate of moments in stochastic approximation with simultaneous perturbation gradient approximation and resetting, IEEE Transactions on Automatic Control, 44, 894-905, 1999. (convergence conditions for moments of SPSA iterate).
Gerencsér L., G. Kozmann, and Z. Vágó, The use of the SPSA method in ECG analysis, IEEE Transactions on Biomedical Engineering, 1998, (application in classification problem), 1998.
Gidas B., Random Media, IMA Math. Appl., 7, 1987, 129-145.
Gilks W., S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman & Hall, 1996.
Glasserman P., Regenerative derivatives of regenerative sequences, Adv. in Appl. Prob., 25, 116-139, 1993.
Glasserman P., Structural conditions for perturbation analysis of queuing systems, Journal of the ACM, 38, 1991, 1005-1025.
Glasserman P., Structural conditions for perturbation analysis derivative estimates: Finite time performance indices, Operation Research, 39, 724-738, 1991.
Glasserman P., Stochastic monotonicity and conditional Monte Carlo for likelihood ratios, Advances in Applied Probability, 25, 103-115, 1993.
Glasserman P., and P. Glynn, Gradient estimation for regenerative processes, in the Proceedings of the Winter simulation conference, 1992, 280-288.
Glasserman P., and W. Gong, Smoothed perturbation analysis for a class of discrete event systems, IEEE Transactions on Automatic Control, 32, 1989, 1218-1230.
Glassserman P., J-Q. Hu, and S. Strickland, Strongly consistent steady-state derivative estimates, Probability in the Engineering and Informational Sciences, 5, 391-413,1991.
Glasserman P. and T. Liu, Rare-event simulation for multistage production-inventory systems, Manage. Sci., 42, 1292-1307, 1995.
Glasserman P., and P. Vakili, Correlation of uniformized Markov chains simulated in parallel, Proceeding of the Winter Simulation Conference, 1992, 412-419.
Glasserman P., and D. Yao, Some guidelines and guarantees for common random numbers, Management Science, 38, 1992, 884-908.
Glasserman P., and D. Yao, Algebraic structural of some stochastic discrete event systems with applications, Journal of Discrete Event Dynamic Systems, 1, 1-23, 1991.
Glover F., Special TABU SEARCH Issue, European Journal of Operational Research, 106(2-3), 1998.
Glover F., J. Kelly, and M. Laguna, New advances and applications of combining simulation and optimization, in the Proceedings of the Winter Simulation conference, 1996.
Glynn P., Independent sampling of a stochastic process, Stochastic Processes and Their Applications, 74, 1998, 151-164.
Glynn P., Likelihood ratio gradient estimation for stochastic systems, Communications of the ACM, 33, 75-84, 1990.
Glynn P., Likelihood ratio gradient estimation: An overview, in the Proceedings of the 1987 Winter Simulation, 366-375, 1987.
Glynn, P., Optimization of stochastic systems, Proceedings of the Winter Simulation Conference, 356-365, 1986.
Glynn P., and P. Heidelberger, Bias properties of budget constrained simulations, Operations Research, 38, 1990, 801-814.
Glynn P., and P. Heidelberger, Analysis of parallel replicated simulations under a completion time constraint, ACM Transactions on Modeling and Computer Simulation, 1, 3-23, 1991.
Glynn, P. and D. Iglehart, Importance sampling for stochastic simulations, Management Science, 35, 1367-1392, 1989.
Glynn P. and D. Iglehart, Simulation methods for queues: An overview, Queueing Systems, 3, 221-256, 1988.
Glynn P., and J. Sanders J., Monte Carlo optimization of stochastic systems: Two new approaches, Proceedings of the ASME Computers in Engineering Conference, 75-80, 1986.
Glynn P., and W. Whitt, The asymptotic efficiency of simulation estimators, Operations Research, 40, 505-520, 1992.
Glynn P., and W. Whitt, The asymptotic validity of sequential stopping rules for stochastic simulations, Annals of Applied Probability, 2, 1992, 180-198.
Goldsman, D., Meketon M., and L. Schruben, Properties of standardized time series weighted area variance estimators, Management Science, 36, 602-12. 1990.
Goldsman D., Nelson B., and B. Schmeiser, Methods for selecting the best systems, Proc. Winter Simulation Conf., 1991, 3-23.
Goldsman D., and L. Schruben, New confidence interval estimators using standardized time series, Management Science, 36, 393-97, 1990.
Goldman F., The application of simulated annealing for optimal operation of water distribution systems, Doctoral Dissertation, Arizona State University, 1998.
Goldstein, L., On the choice of step size in the Robbins-Monro procedure, Statistics & Probability Letters, 6, 299-303, 1988.
Golenko-Ginzburg D., A. Gonik, L. Papic, Developing cost-optimization production control model via simulation, Mathematics And Computers In Simulation, 49, 1999, 335-351.
Gong W., and Y. Ho, Smoothed (conditional) perturbation analysis of discrete event dynamic systems, IEEE Transactions on Automatic Control, AC-32, 1987, 858-866.
Gong W., Ho Y-C., and W. Zhi, Stochastic Comparison algorithm for Discrete Optimization with Estimation, Proc. of 1st IEEE Conference on Decision and Control, 1992, 795-800.
Goovaerts P., Stochastic simulation of categorical variables using a classification algorithm and simulated annealing, Mathematical Geology, 28, 909-921, 1996.
Gorelick s., Large scale nonlinear deterministic and stochastic optimization: Formulations involving simulation of subsurface contamination., Mathematical Programming, Ser. B, 48, 1990, 19-39.
Gourieroux C., and A. Monfort, Simulation-Based Econometric Methods, Oxford University Press, 1997.
Goyal A., P. Shahabuddin, P. Heidelberger, V. Nicola, and P. Glynn, A unified framework for simulating Markovian models of highly dependable systems, IEEE Trans. Comput., C-41, 36-51, 1992.
Greasley A., Using process mapping and business process simulation to support a process-based approach to change in a public sector organization, Technovation, 26(1), 2006, 95-103.
Greenwood A., An investigation of the behavior of simulation response surfaces, Eur. J. Operational Research, 111, 1998, 282-313.
Gross D., and C. Harris, Fundamentals of Queueing Theory, Wiley, New York, 1998.
Grubmann N., BESMOD: A strategic balance sheet simulation model, European Journal of Operational Research, 30, 30-34, 1987.
Gruer P., Modeling and quantitative analysis of discrete event systems: A statecharts based approach, Simulation Practice and Theory, 6, 1998, 397-411.
Guariso G., M. Hitz, and H. Werthner, An integrated simulation and optimization modelling environment for decision support, Decision Support Systems, 16, 103-117, 1996.
Guide Jr. V., and R. Srivastava, Repairable inventory theory: Models and applications, Eur. J. Operational Research, 102, 1997, 1-20.
Gülpnar N., Rustem B., and R. Settergren, Simulation and optimization approaches to scenario tree generation, Journal of Economic Dynamics and Control, 28(7), 2004, 1291-1315.
Gurkan G., A. Ozge, and S. Robinson, Sample path solution of stochastic variational inequalities, Mathematical Programming, 84, 313-333, 1999.
Gutjahr W., and G. Pflug, Simulated annealing for noisy cost functions, Journal of Global Optimization, 8, 1996, 1-13.

Haas P., On simulation output analysis for generalized semi-Markov processes, Comm. Statist. Stochastic Models, 15, 53-80, 1999.
Haas P., and G. Shedler, Regenerative generalized semi-Markov processes, Comm. Statist. Stochastic Models, 3, 409-438, 1987.
Haas P., and G. Shedler, Recurrence and regeneration in non-Markovian networks of queues, Comm. Statist. Stochastic Models, 3, 29-52, 1987.
Haas P., and G. Shedler, Stochastic Petri Net representation of discrete event simulations, IEEE Trans. Software Engrg., 15, 381-393, 1989.
Haas P., and G. Shedler, Estimation methods for passage times using one-dependent cycles, Discrete Event Dynam. Systems Theory Appl., 6, 43-72, 1996.
Haddock J., and J. Mittenhall, Simulation optimization using simulated annealing, Computers and Industrial Engineering, 22, 387-395, 1992.
Hartmann A., and H. Schwetman, Discrete-event simulation of computer and communication systems, Chapter 20, pp. 659-676, in Handbook of Simulation, Banks J., (Ed.), John Wiley, 1998.
Hatfield D., and G. Curry, On optimizing functions which are defined in part by an approximation process, Mathematical Programming, 20, 63-80, 1981.
Haurie A., P. L'Ecuyer, and C. van Delft, Convergence of stochastic approximation coupled with perturbation analysis in a class of manufacturing flow control models, Discrete Event Dynamic Systems: Theory and Applications, 4, 1994, 87-111.
Hazra M., D. Morrice, and S. Park, A simulation clock-based solution to the frequency domain experiment indexing problem, IIE Transactions, 29, 769-782, 1997.
Heh J., Evaluation model of problem solving, Mathematical and Computer Modelling, 30, 197-211, 1999.
Heidenberger K., Schillinger A., and Ch. Stummer, Budgeting for research and development: A dynamic financial simulation approach, Socio-Economic Planning Sciences, 37(1), 2003, 15-27.
Heidelberger P., X. Cao, M. Zazanis, and R. Suri, Convergence properties of infinitesimal perturbation analysis estimates. Management Science, 34, 1281-1302, 1988.
Heidelberger P., P. Shahabuddin, and V. Nicola, Bounded relative error in estimating transient measures of highly dependable non-Markovian systems, ACM Transactions on Modeling and Computer Simulation, 4, 137-164, 1994.
Heidelberger P. and D. Towsley, Sensitivity analysis from sample paths using likelihoods, Management Science, 35, 1475-1488, 1989.
Heidergott B., Sensitivity analysis of a manufacturing workstation using perturbation analysis techniques, International Journal of Production Research, 37, 611-622, 1995.
Heine G., Smart Simulated Annealing, Ph.D. Dissertation, University of Colorado, Denver, 1994.
Heller N., and G. Staats, Response surface optimization when experimental factors are subject to costs and constraints, Technometrics, 15, 113-123, 1973.
Hellinck W., Experiences with combining constraint programming and discrete event simulation, In Freuder E. (editor.), Principles and Practice of Constraint Programming, Lecture Notes in Computer Science, 1118, 543-544, 1996.
Hickernell F., A comparison of random and quasirandom points for multidimensional quadrature, in Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (H. Niederreiter and P. J.-S. Shiue, eds.), Lecture Notes in Statistics, 106, Springer-Verlag, New York, 213-227, 1995.
Higle J., and S. Sen, Statistical approximations for stochastic linear programming problems, Annals of Operations Research, 84, 205-224, 1998.
Higle J., W. Lowe, and R. Odio, Conditional stochastic decomposition: An algorithmic interface for optimization and simulation, Operations Research, 42, 1994, 311-322.
Hill S., and M. Fu, Transfer optimization via simultaneous perturbation stochastic approximation, Proceedings of the Winter simulation conference, 1995, 242-249.
Hill T., and S. Roberts, A prototype knowledge based simulation support system, Simulation, 48, 152-161, 1987.
Ho Y., A Survey of the Perturbation Analysis of Discrete Event Dynamic Systems, Annals of Operations Research, 3, 1985, 393-402.
Ho Y., Discrete Event Dynamic Systems: Analyzing Complexity and Performance in the Modern World, IEEE press, 1992.
Ho Y., A new paradigm for stochastic optimization and parallel simulation, in Discrete Event Systems, Manufacturing Systems, and Communication Networks, (Eds.) Kumar P, and P. Varaiya, IMA Volume 73, Springer-Verlag, 1993.
Ho Y., Heuristics, rules of thumb, and the 80/20 proposition, IEEE Trans. on Automatic Control, 39,1025-1027, 1994.
Ho Y., On the numerical solution of stochastic optimization problems, IEEE Trans. on Automatic Control, 42, 1997, 727-729. Computational Limits of Simulation as a tool for performance evaluation and optimization. And what can be done.
Ho Y., and X. Cao, Perturbation analysis and optimization of queueing networks, Journal of Optimization Theory and Applications, 40, 1983, 559-582.
Ho Y., and X. Cao, Optimization and perturbation analysis of queueing networks, J. Optim. Theory Appl., 40, 1983, 559-582.
Ho Y., X. Cao, and C. Cassandras, Infinitesimal and finite perturbation analysis for queueing networks, Automatica, 19, 439-445, 1983.
Ho Y., and C. Cassandras, A new approach to the analysis of discrete event dynamic systems, Automatica, 19, 1983, 149-167.
Ho Y., C. Cassandras, C. Chen, and L. Dai, Ordinal optimisation and simulation, Journal of the Operational Research Society, 51, 490-500, 2000.
Ho Y., M. Eyler, and T. Chien, A gradient technique for general buffer storage design in a serial production line, Int. J. Product. Res., 17, 1970, 557-580.
Ho Y., M. Eyler, and T. Chien, A new approach to determine parameter sensitivity of transfer lines, Manag. Sci., 29, 1983, 700-714.
Ho Y., and M. Larson, Ordinal optimization approach to rare event probability problems, J. Discrete Event Dynamic Systems, 5, 281-301, 1995.
Ho Y., S. Leyuan, D. Liyi, and W. Gong W., Optimizing discrete event dynamic systems via the gradient surface method, Discrete Event Dynamic Systems: Theory and Applications, 2, 99-120, 1992.
Ho Y. and S. Li, Extensions of perturbation analysis of discrete event dynamic systems, IEEE Transactions on Automatic Control, 33, 1988, 427-438.
Ho Y., S. Li, and P. Vakili, On the efficient generation of discrete event sample paths under different system parameters, Math. Comp. Simul., 30, 1988, 347-370.
Ho Y., L. Shi, and L. Dai, Optimizing discrete event dynamic systems via the gradient surface method, Discrete Event Dynamic Systems: Theory and Applications, 2(2), 1992.
Ho Y., R. Sreenivas, and P. Vakili, Ordinal optimization of discrete event dynamic systems, J. of DEDS, 2, 61-88, 1992.
Holden L., Geometric convergence of the Metropolis-Hastings simulation algorithm, Statistics and Probability Letters, 39, 1998, 371-377.
Holland J., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
Hooke R. and T. Jeeves, A direct search solution of numerical and statistical problems, Journal of Association for Computing Machinery, 8, 212-229, 1961.
Hoppensteadt F., Analysis and Simulation of Chaotic Systems, Springer, 1993.
Holst L., and G. Bolmsjö, Simulation integration in manufacturing system development: A study of Japanese industry, Industrial Management & Data Systems, 101, 339-356, 2001.
Horibe D., Application of smoothed perturbation analysis to a discrete-time stationary queue, Journal of the Operations Research Society of Japan, 41, 1998, 152-165.
Hsieh S-J., Hybrid analytic and simulation models for assembly line design and production planning, Simulation Modelling Practice and Theory, 10(1-2), 2002, 87-108.
Hsu J., and B. Nelson, Optimization over a finite number of system designs with one-stage sampling and multiple comparison with the best, Proceeding of the Winter Simulation Conference, 451-457, 1988.
Hsu L., C. Tapiero, and C. Lin, Network of queues modelling in flexible manufacturing systems: A survey, Recherche Operationnelle, 27, 202-248, 1991.
Hu J-Q., Convexity of sample path performances and strong consistency of infinitesimal perturbation analysis estimates, IEEE Transactions on Automatic Control, 37, 1992, 258-262.
Hu J-Q., P. Vakili, and G. Yu, Optimality of hedging point policies in the production control of failure prone manufacturing systems, IEEE Transactions on Automatic Control, 39, 1994, 1875-1880.
Hu J-Q., and D. Xiang, Structural properties of optimal controllers for failure prone manufacturing systems, IEEE Transactions on Automatic Control, 39, 1994, 640-642.
Hu N., Tabu search with random moves for globally optimal design, Int. J. for Numerical Methods in Engrg., 35, 1992, 1055-1070.
Hung W-Y., N. Samsatli, and N. Shah, Object-oriented dynamic supply-chain modelling incorporated with production scheduling, European Journal of Operational Research, 169(3), 2006, 1064-1076.
Hurrion R., An example of simulation optimization using a neural network metamodel: Finding the optimum number of kanbans in a manufacturing system, Journal of the Operational Research Society, 48, 1105-1112, 1997.
Hurrion R., Visual interactive meta-simulation using neural networks, International Transactions in Operational Research, 5, 261-27.
Hussain M., R. Barton, and S. Joshi, Metamodeling: Radial basis functions, versus polynomials, European Journal of Operational Research, 138, 142-154, 2002.
Huynh H., and S. Kumar, A random search method for extreme point mathematical programming, Asia-pacific Journal of Operational Research, 7, 30-45, 1990.

Iglehart D., and G. Shedler, Regenerative Simulation of Response Times in Networks of Queues, Springer-Verlag, Berlin, 1980.

Jacobson S., Optimal mean squared error analysis of the harmonic gradient estimators, Journal of Optimization Theory and Application, 80, 573-590, 1994.
Jacobson S., Discrete optimization and selection, in the Proceedings of the Winter Simulation conference, 1996.
Jacobson S., Convergence results for harmonic gradient estimators, ORSA Journal on Computing, 6, 381-397, 1994.
Jacobson S., Second derivative estimation using harmonic analysis, Annals of Operations Research, 53, 507-531, 1994.
Jacobson S., Analyzing the M/M/1 queue in frequency domain experiments, Applied Mathematics and Computation, 69, 185-194, 1995.
Jacobson S., Variance and bias reduction techniques for the harmonic gradient estimation, Applied Mathematics and Computation, 55, 153-186, 1993.
Jacobson S., The effect of initial transient on steady state simulation harmonic gradient estimators, Mathematics and Computers in Simulation, 43, 209-221, 1997.
Jacobson S., Buss A., and L. Schruben, Driving frequency selection for frequency domain simulation experiments, Operations Research, 39, 917-924, 1991.
Jacobson S., and L. Schruben, Techniques for Simulation Response Optimization, Operations Research Letters, 8, 1-9, 1989.
Jacobson S., and L. Schruben, A harmonic analysis approach to simulation sensitivity analysis, IIE Transactions, 31, 231-243, 1999.
Jarzemba M., and B. Sagar, A parameter tree approach to estimating system sensitivities to parameter sets, Reliability Engineering and System Safety, 67, 89-102, 2000.
Jaulin L., J-L. Boimond, and L. Hardouin, Estimation of discrete-event systems using interval computation, Reliable Computing, 5, 165-173, 1999.
Jávor A., Szu"cs G., Simulation and optimization of urban traffic using AI, Mathematics And Computers In Simulation, 46, 1998, 13-21.
Jeong K-Y., Conceptual frame for development of optimized simulation-based scheduling systems, Expert Systems with Applications, 18, 299-306, 2000.
Johnson D., C. Aragon, L. McGeoch and C. Schevon, Optimization by simulated annealing: An experimental evaluation, Part 2, Graph coloring and number partitioning, Operations Research, 39, 1991, 378-406.
Johnson M., and J. Jackman, Infinitesimal Perturbation Analysis: A Tool For Simulation, Journal of Operational Research society, 40, 243-254, 1989.
Jones Ch., Visualization and Optimization, Kluwer Academic Pub., 1996.
Jonsbråten T., R. Wets, and D. Woodruff, A class of stochastic programs with decision dependent random elements, Annals of Operations Research, 82, 1998, 83-106.
Joshi S., A. Rathi, and J. Tew, An improved response surface methodology algorithm with an application to traffic signal optimization for urban networks, in the Proceedings of the Winter Simulation conference, 1995.
Jun C., and S. Ross, System reliability by simulation: Random hazards versus importance sampling, Probability in the Engineering and Informational Sciences, 6, 1992, 119-126.

Kalashnikov V., and V. Sedunov, Sensitivity analysis of regenerative queueing models, Queueing Systems, 19, 1995, 247-268.
Kalasky D., Simulation-based supply-chain optimization for consumer products, in the Proceedings of the Winter Simulation conference, 1996.
Kalymon B., An optimization algorithm for a linear model of a simulation system, Management Science, 21, 1975, 516-530.
Kamrani A., K. Hubbard, H. Parsaei, and H. Leep, Simulation-based methodology for machine cell design, Computers and Industrial Engineering, 34, 1998, 173-188.
Kao C., W. Song, and S. Chen, A modified quasi-newton method for optimization in simulation, International Transactions in Operational Research, 4, 223-233, 1997.
Karacal S., A novel approach to simulation modeling, Computers and Industrial Engineering, 34, 573-587, 1998.
Karim A., J. Hershauer, and W. Perkins, A simulation of partial information use in decision making: Implications for DSS design, Decision Sciences, 29, 1998, 53-85.
Kashyap R., C. Blaydon, and K. Fu, Stochastic approximation," in A Prelude to Neural Networks: Adaptive and Learning Systems , J. Mendel (ed.), Prentice Hall, 329-355, 1994.
Kesten H., Accelerated stochastic approximation, Ann. Math. Statist., 29, 1958, 41-59.
Keys A., L. Rees, and A. Greenwood, Performance measures for selection of metamodels to be used in simulation optimization, Decision Sciences Journal, 33, 31-57, 2002.
Khazen M., and A. Dubi, A note on variance reduction methods in Monte Carlo applications to systems engineering and reliability, Monte Carlo Methods and Applications, 5, 345-379, 1999.
Khobotov E., The optimization simulation approach to modeling of sophisticated manufacturing systems, II, Journal of Computer and Systems Sciences International, 35, 273, 1996.
Khranovich I., Simulation of optimal development for water-supply systems. II: A flow approach, Autom. Remote Control, 45, 1984, 1346-1353
Kiefer J., and J. Wolfowitz, Stochastic estimation of the maximum of a regression function, Annals of Mathematical Statistics, 23, 462-466, 1952.
Kil R., and Y. Song, Random search on genetic operators, Simulated Evolution and Learning, Lecture Notes in Artificial Intelligence, Vol. 1285, 196-205, 1996.
Kilmer R., A. Smithg, and L Schuman, Computing confidence intervals for stochastic simulation using neural network metamodels, Computers & Industrial Engineering, 36, 391-407, 1999.
Kim C. and H. Blake, An experimental comparison of simulation optimization techniques, International Journal of Modelling & Simulation, 8, 22-28, 1988.
Kirkpatrick S., Gellat Jr., C., and M. Vecchi, Optimization by simulated annealing, Science, 220, 671-680, 1983.
Klebaner F., Moderate deviations for randomly perturbed dynamical systems, Stochastic Processes and their Applications, 80, 157-176, 1999.
Kleijnen J., Regression metamodels for simulation with common random numbers: Comparison of validation tests and confidence intervals, Management Science, 38, 1992, 64-85.
Kleijnen, J., Regression metamodel for generalizing simulation results, IEEE Transaction on Systems, Man and Cybernetics, 9, 93-96, 1979.
Kleijnen J., Statistical Tools for Simulation Practitioners, New York Marcel Dekker, Inc., 1987.
Kleijnen J., Sensitivity analysis and optimization of system dynamics models: regression analysis and statistical design of experiments, System Dynamics Review, 11, 275- 288, 1995.
Kleijnen J., Sensitivity analysis and related analyses: a review of some statistical techniques, Journal of Statistical Computation and Simulation, 57, 1997, 111-142.
Kleijnen J., A methodology for fitting and validating metamodels in simulation, European Journal of Operational Research, 120, 14-29, 1999.
Kleijnen J., C. Helton, Statistical analyses of scatterplots to identify important factors in large-scale simulations, Reliability Engineering and System Safety, 65, 147-197, 1999.
Kleijnen J., and R. Rubinstein, Optimization and sensitivity analysis of computer simulation models by score function method, European Journal of Operational Research, 88, 413-427, 1996.
Kleijnen J., and P. Standridge, Experimental design and regression analysis in simulation: An FMS case study, European Journal of Operational Research, 33, 1988, 257-261.
Kljajic M., I. Bernik, and A. Skraba, Simulation Approach to Decision Assessment in Enterprises, Simulation, 75, 199-210, 2000.
Koehler G., New directions in genetic algorithm theory, Annals of Operations Research 75, 49-68, 1997.
Koltai Tamas, Lozano Sebastian, Sensitivity calculation of the throughput of an FMS with respect to the routing mix using perturbation analysis, Eur. J. Operational Research, 105, 1998, 483-493.
Konstantopoulos P., and M. Zazanis, Sensitivity analysis for stationary and ergodic queues, Adv. in Appl. Prob., 24, 738-750, 1992.
Korn G., Real statistical experiments can use simulation-package software, Simulation Modelling Practice and Theory, 13, 39-54, 2005.
Kouikoglou V., and Y. Phillis, An exact discrete-event model and control policies for production lines with buffers, IEEE Transactions on Automatic Control, 36, 1991, 515-527.
Kouikoglou V., and Y. Phillis, Discrete event modeling and optimization of production lines with random rates, IEEE Transactions on Robotics and Automation, 10, 1994, 153-159.
Kouikoglou V., and Y. Phillis, A continuous-flow model for production networks with finite buffers, unreliable machines, and multiple products, International Journal of Production Research, 35, 1997, 381-397.
Krauth J., and R. Schaback, An interactive system for simulation and graphic evaluation of discrete and continuous models, The First European Simulation Congress, 1983.
Kreimer J., Generalized estimates for performance Sensitivities of Stochastic Systems, Mathematical and Computer Modelling, 10, 1988, 911-922.
Kreimer J., Generalized sensitivity analysis of ergodic stochastic systems, Math. and Comp. in Simulation, 31, 1989, 123-136.
Kriman V., Sensitivity analysis of GI/GI/m/B queues with respect to buffer size by the score function method, Stochastic Models, 39, 171-194, 1995
Kuk, A. The use of approximating models in Monte Carlo maximum likelihood estimation, Statistics and Probability Letters, 45, 325-333, 1999.
Kumar S., and M. Talukder, Path in a protean communication network, Chapter 23, pp. 215-225, in Recent Development in Operational Research, M. Agarwal and K Sen (eds.), Narosa Publishing House, India, 2001.
Kushner H., and A. Shwartz, An invariant measure approach to the convergence of stochastic approximations with state dependent noise, SIAM Journal on Control and Optimization, 22, 1984, 13-27.
Kushner H., and G. Yin, Asymptotic properties of distributed and communicating stochastic approximation algorithms, SIAM Journal on Control and Optimization, 25, 1987, 1266-1290.
Kwon C., and J. Tew, Strategies for combining antithetic variates and control variates in designed simulation experiments, Management Science, 40, 1994, 1021-34.

Lacksonen T., Empirical comparison of search algorithms for discrete event simulation, Computers and Industrial Engineering, 40, 133-148, 2001.
Lagergren M., What is the role and contribution of models to management and research in the health services?, Eur. J. Operational Research, 105, 1998, 257-266.
Lai T., and H. Robbins, Adaptive design and stochastic approximation, Annals of Statistics, 7, 1979, 1196-1221.
Lamb J., and R. Cheng, Optimal allocation of runs in a simulation metamodel with several independent variables, Operations Research Letters, 30, 189-194, 2002.
Larsen C., Investigating sensitivity and the impact of information on pricing decisions in an M/M/1/∞ queueing model, Int. J. Production Economics, 56-57, 1998, 365-377.
Lau T., and Y. Ho, Universal alignment probabilities and subset selection for ordinal optimization, Journal of Optimization Theory and Applications, 93, 455-489, 1997.
L'Ecuyer P., A unified view of the IPA, SF and LR gradient estimation techniques, Management Science, 36, 1990, 1364-1383.
L'Ecuyer P., On the interchange of derivative and expectation for likelihood derivative estimation, Management Science, 41, 1995, 738-748.
L'Ecuyer P., Note: On the interchange of derivative and expectation for likelihood ratio derivative estimator, Management Science, 41, 738-748, 1995.
L'Ecuyer P., Convergence rates for steady-state derivative estimators, Ann. Oper. Res., 39, 121-137, 1992.
L'Ecuyer P., Giroux N., and P. Glynn P., Stochastic optimization by simulation: Some experiments with the M/M/1 queue in steady-state queue, Management Science, 40, 1994, 1245-1261.
L'Ecuyer P., and P. Glynn, Stochastic optimization by simulation: Convergence proofs for the GI/G/1 queue in steady-state, Management Science , 40, 1562-1578, 1994
L'Ecuyer P., and F. Vázquez-Abad, Functional estimation with respect to a threshold parameter', DEDS: Theory and Appl., 7, 1997, 69-92.
L'Ecuyer P., and G. Perron, On the convergence rates of IPA and FDC derivative estimators, Operations Research, 42, 1994, 643-656.
L'Ecuyer P., and G. Yin, Rates of convergence for budget dependent stochastic optimization algorithms, Proceedings of the 35^th IEEE Conference on Decision and Control, 1069-1070, 1996.
Lee J., Faster simulated annealing techniques for stochastic optimization problems, with application to quereing network simulation, PhD. Dissertation, Statistics and Operations Research, North Carolina State University, 1995.
Lee L., T. Lau., and Y. Ho, Explanation of goal softening in ordinal optimization, IEEE Transactions on Automatic Control, 44, 94-98, 1999.
Lee Y., and K. Iwata, Part ordering through simulation-optimization in an FMS, Journal of the Operational Research Society, 29, 1991, 1309-1323.
Lee Y., K. Kyung, and C. Jung, On-line determination of steady state in simulation outputs, Computers & Industrial Engineering, 33, 805-808, 1997.
Lee Y., and K. Lawate, Part ordering through simulation-optimization in an FMS, International Journal of Production Research, 29, 1309-1323, 1991.
Lee Y., K-J. Park, and Y. Kim, Single run optimization using the reverse-simulation method, in the Proceedings of the Winter Simulation Conference, 187-193, 1997.
Lehtonen T., and H. Nyrhinen, Simulating level crossing probabilities by importance sampling, Adv. Appl. Probab., 24, 858-874, 1992
Legato P., and R. Mazza, Berth planning and resources optimisation at a container terminal via discrete event simulation, European Journal of Operational Research, 133, 537-547, 2001.
Lei X., E. Lerch, D. Povh, and B. Kulicke, Optimization: A new tool in simulation program system, IEEE Power Engineering Review, 17, p55, 1997.
Leung T., C. Chan, and M. Troutt, Application of a mixed simulated annealing-genetic algorithm heuristic for the two-dimensional orthogonal packing problem, European Journal of Operational Research, 145, 530-542, 2003.
Leung Y., Single-Run Optimization of Discrete-Event Simulation, Ph.D. Dissertation, University of Wisconsin, 1990.
Leung D., and Wang Y-G., Bias reduction using stochastic approximation, Australian & New Zealand Journal of Statistics, 40, 43-52, 1998.
Levitin G., and A. Lisnianski, Joint redundancy and maintenance optimization for multistate series-parallel systems, Reliability Engineering and System Safety, 64, 33-42, 1999.
Li W., On stochastic machine scheduling with general distributional assumptions, European Journal of Operational Research, 105, 1998, 525-536.
Lieberman G., and S. Ross, On the variance of the hazard estimator in simulation, Probability in the Engineering and Informational Sciences, 5, 1991, 355-359.
Lien G., Assisting whole-farm decision-making through stochastic budgeting, Agricultural Systems, 76(2), 2003, 399-413.
Lisnianski A., G. Levitin, and H. Ben-Haim, Structure optimization of multi-state system with time redundancy, Reliability Engineering and System Safety, 67, 103-112, 2000.
Liu C., and J. Sanders, Stochastic design optimization of asynchronous flexible assembly systems, Annals of Operations Research, 15, 131-154.
Liu Y., and W. Gong, Perturbation analysis for stochastic fluid queueing systems, Discrete Event Dynamic Systems, 12, 391-416, 2002.
Ljung L., Analysis of recursive stochastic algorithms, IEEE Trans, Auto. Control, AC-22, 1977, 551-575.
Ljung L., Strong convergence of a stochastic approximation algorithm, Ann. Statist., 6, 1978, 680-696.
Ljung L., Pflug G., and H. Walk, (Editors), Stochastic Approximation and Optimization of Random Systems, Birkhauser, Basel, 1992.
Løvås G., Models of wayfinding in emergency evacuations, Eur. J. Operational Research, 105, 1998, 371-389.
Lüthi J., and G. Haring, Mean value analysis for queueing network models with intervals as input parameters, Performance Evaluation, 32, 1998, 185-215.
Luus R., Optimization of multistage recycle systems by direct search, Canadian J. of Chem. Eng., 53, 1975, 217-229.
Luus R., and T. Jakola, Optimization by direct search and systematic reduction of the size of search region, AIChE Journal, 19, 1973, 760-766.

Madu I., Design optimization using signal-to-noise ratio, Simulation Practice and Theory, 7. 349-372, 1999.
Maffioli F., M. Speranza, and C. Vercellis, Randomized algorithms: An annotated bib liography, Ann. Oper. Res., 1, 331-345, 1984.
Mansour M., and J. Ellis, Comparison of methods for estimating real process derivatives in on-line optimization, Applied Mathematical Modelling, 27, 275-291, 2003.
Marqueza A., and C. Blanchar, A Decision Support System for evaluating operations investments in high-technology business, Decision Support Systems, 41(2), 2006, 472-487.
Marti K., Stochastic optimization methods of structural design, ZAMM, 4, T742-T745, 1990.
Marti K., Computation of efficient solutions of discretely distributed stochastic optimization problems, Z. Oper. Res., 36, 1992, 259-294.
Marti K., and E. Fuchs, Computation of descent directions and efficient points in stochastic optimization problems without using derivatives, Math. Program. Study, 28, 1986, 132-156.
Martorell S., S Carlos, A. Sanchez, and V. Serradell, Constrained optimization of test intervals using a steady-state genetic algorithm, Reliability Engineering and System Safety, 67, 215-232, 2000.
Matejcik F., and B. Nelson, Two-stage multiple comparisons with the best for computer simulation, Operations Research, 43, 1995, 633-640.
Mathé P., Numerical integration using Markov chains, Monte Carlo Methods and Applications, 5, 325-344, 1999.
Mayer D., J. Belward, and K. Burrage, Optimizing simulation models of agricultural systems, Annals of Operations Research, 82, 1998, 219-232.
McLeish D. and Rollans S., Condition for variance reduction in estimating the sensitivity of simulations, Annals of Operations Research, 39, 1992, 157-172.
Mebarki N., A. Dussauchoy, and H. Pierreval, On the comparison of solutions in stochastic simulation-optimization problems with several performance measures, International Transactions In Operational Research, 5, 137-145, 1998.
Meeuwissen A., and T. Bedford, Minimally informative distributions with given rank correlation for use in uncertainty analysis, Journal of Statistical Computation and Simulation, 57, 1997, 143- 174.
Meier R., The application of optimal-seeking techniques to simulation studies: A preliminary evaluation, Journal of Financial and Quantitative Analysis, 2, 1967, 31-51.
Meketon M., Optimization in simulation: A survey of recent results, Proceedings of the Winter Simulation Conference, 58-67, 1987.
Melas V., On the efficiency of the splitting and roulette approach for sensitivity analysis, in the Proceedings of the Winter Simulation Conference, 269-274, 1997.
Merkuryev Y. Integral optimization in simulation modeling of discrete systems, Automatic control and computer sciences, 31, 27-38, 1997.
Merkuryev G., and Y. Merkuryev, Knowledge based simulation systems-A review, Simulation, 62, 74-89, 1994.
Merkuryev Y., L. Rastrigin, and V. Visipkov, Deterministic experiments in optimization of stochastic simulation systems, Proceedings of the 1995 European Simulation Multiconference, 49-53, 1995.
Merkuryev Y., L. Rastrigin, and V. Visipkov, Knowledge-based selection and adaptation of optimization algorithms in discrete system simulation, Proceedings of the 1995 Summer Computer Simulation Conference, Ed. by Louis Birta and Tuncer Oren. 1995, 82-85.
Merkuryev Y., and V. Visipkov, A survey of optimization methods in discrete systems simulation, First Joint Conference of International Societies Proceedings, 104-110, 1996.
Merkuryev Y., and V. Visipkov, Two-stage optimization of discrete-event simulation models, Proceedings the European Simulation Symposium, 24-26, 1996.
Michalewicz Z., Evolutionary computation techniques for non-linear programming problems, International Transactions in Operational Research, 1, 1994, 233-240.
Miller D. R., Sensitivity analysis and validation of simulation models, Journal of Theoretical Biology, 48, 1974, 345-360.
Minkoff M, Approaches to optimization/simulation problems, Applied Numerical Mathematics, 3, 453-466, 1987.
Mitra M., and S. Park, Solution to the indexing problem of frequency domain simulation experiments, Proceedings of the Winter Simulation Conference, 1991, 907-915.
Mnif M., and H. Pham, Stochastic optimization under constraints, Stochastic Processes and their Applications, 93, 149-180, 2001.
Monga A., and M. Zuo, Optimal design of series-parallel systems considering maintenance and salvage value, Computers and Industrial Engineering, 40, 323-337, 2001.
Monroe H., and R. Sielken, Confidence limits for global optima based on heuristic solutions to difficult optimization problems: A simulation study., Am. J. Math. Manage. Sci., 4, 1984, 139-167.
Montgomery D., and D. Evans, Second order response surface design in computer simulation, Simulation, 25, 1975, 169-178.
Morrice D., and I. Bardhan, A weighted least squares approach to computer simulation factor screening, Operations Research, 43, 792-806, 1995.
Morrice D., and Sh. Jacobson, Amplitude selection in transient sensitivity analysis, in the Proceedings of the Winter Simulation conference, 1995.
Myers D., and A. Yeh, Generating correlated random variables for a simulation model, Journal of the Operational Research Society, 50, 183-186, 1999.

Nakayama H., Simulation-based optimization using computational intelligence, Optimization and Engineering, 3, 201-214, 2002.
Nakayama M., Asymtotics of likelihood ratio derivative estimators in simulations of highly reliable Markovian systems, Management Science, 41, 524-544, 1995.
Nakayama M, Multiple-comparison procedures for steady-state simulations. Annals of Statistics, 25, 2433-2450, 1997.
Nakayama M., Multiple comparisons with the best using common random numbers in steady-state simulations, Journal of Statistical Planning and Inference, 85, 37-48, 2000.
Nakayama M., A. Goyal, and P. Glynn, Likelihood ratio sensitivity analysis for Markovian models of highly dependable systems, Operations Research, 42, 137-157, 1994.
Nakayama M., and P. Shahabuddin, Likelihood ratio derivative estimation for finite-time performance measures in generalized semi-Markov processes, Management Science, 44, 1426-1441, 1998.
Nelder J., and R. Mead, A simplex method for function minimization, Computer Journal , 7, 1965, 308-313.
Nelson B., and J. Hsu, Control-variate models of common random numbers for multiple comparisons with the best, Management Science, 39, 1993, 989-1001.
Nelson B., and F. Matejcik, Using common random numbers for indifference-zone selection and multiple comparisons in simulation, Management Science, 41, 1935-1945, 1995.
Nevel'son M., and R. Has'minskii, An adaptive Robbins-Monro procedure, Automation and Remote Control, 34, 1973, 594-1607.
Nguyen V., and M. Reiman, Variance reduction for sensitivity estimates obtained from regenerative simulation, Operations Research Letters, 14, 1993, 9-18.
Niederreiter H., and P. Peart, Localization of search in quasi-Monte Carlo methods for global optimization, SIAM J. Sci. Statist. Computing, 7, 1986, 660-664.

O"berg T, Importance of the first design matrix in experimental simplex optimization, Chemometrics and Intelligent Laboratory Systems, 44, 151-154, 1998.
Oberkampf W., S. DeLand, B.Rutherford, K. Diegert, and K. Alvin, Error and uncertainty in modeling and simulation, Reliability Engineering & System Safety, 75, 333-357, 2002.
Ockerman D., Student t-tests and compound tests to detect transients in simulated time series, European Journal of Operational Research, 116, 681-691, 1999.
Okada M., S. Hara, S. Komaki, and N. Morinaga, An application of simulated annealing to the design of block coded modulation, IEICE Transactions on Communications, E79-b, 1, 88-91, 1996.
O'Keefe R., Simulation and expert systems: A taxonomy and some examples, Simulation, 46, 1986, 10-16.
Olafsson S., and L. Shi, A method for scheduling in parallel manufacturing systems with flexible resources, IIE Transactions on Scheduling and Logistics, 32, 135-146, 2000.
Ollson D. and L. Nelson, The Nelder-Mead simplex procedure for function minimization, Technometrics, 17, 1975, 45-51.
Osorio M, Gülpnar N., Rustem B., and R. Settergren, Post-tax optimization with stochastic programming, European Journal of Operational Research, 157(1), 2004, 152-168.
Otto J., et al., Bayesian-validated computer-simulation surrogates for optimization and design: Error estimates and applications, Mathematics and Computers in Simulation, 44, 347-367, 1997.
Ouwersloot H., J. Lemmink, and K. de Ruyter, Moving beyond intuition: Managing allocation decisions in relationship marketing in business-to-business markets, Industrial Marketing Management, 33(8), 2004, 701-710.
Ozden M., and Y.-C. Ho, A probabilistic solution-generator for simulation, European Journal of Operational Research, 146, 35-51, 2003.

Pagell M., and S. Melnyk, Assessing the impact of alternative manufacturing layouts in a service setting, Journal of Operations Management, 22(4), 2004, 413-429.
Pang K., Z. Yang, S. Hou, and P. Leung, Non-uniform random variate generation by the vertical strip method, European Journal of Operational Research, 142, 595-609, 2002.
Papageorgiou A., and J. Traub, Faster evaluation of multidimensional integrals, Computers in Physics, 11, 1997, 574-578.
Parekh S., and J. Walrand, A quick simulation method for excessive backlogs in networks of queues, IEEE Trans. Automat. Contr., AC-34, 54-66, 1989.
Park D., Y. Kim, K. Shin, and T. Willemain, Simulation output analysis using the threshold bootstrap, European Journal of Operational Research, 134, 17-28, 2001.
Park J., Optimization techniques using computer simulation, Korea Information Science Society Review, 8, 37-47, 1990.
Park M., and Y. Kim, A systematic procedure for setting parameters in simulated annealing algorithms, Computers and Operation Research, 25, 1998, 207-217.
Park Y., and E. Chong, Distributed inversion in timed discrete event systems, Discrete Event Dynamic Systems: Theory and Applications, 5, 1995, 219-241.
Park Y., and E. Chong, On inversion in interruptive timed discrete event systems, IEEE Transactions on Automatic Control, 42, 1997, 1550-1554.
Parkinson D., Second order stochastic simulation with specific correlation, Advances in Engineering Software, 30, 489-494, 1999.
Patel N. Smith R. and Z. Zabinsky Z., Pure adaptive search in Monte Carlo optimization, Mathematical Programming, 43, 1989, 317-328.
Paul R., Simulation optimisation using a genetic algorithm, Simulation Practice and Theory, 6, 601-611, 1998.
Pearl D., R. Bartoszynski, J. Maa, and D. Horn, High-dimensional simulation-based estimation, Mathematical and Computer Modelling, 32, 113-124, 2000.
Pellizzari P., Static hedging of multivariate derivatives by simulation, European Journal of Operational Research, 166, 507-519, 2005.
Pesonen J., and E. Hyvonen, Interval approach challenges Monte Carlo simulation, Reliable Computing, 2, 155-160, 1996.
Petrovic D., Modelling and simulation of a supply chain in an uncertain environment, European Journal of Operational Research, 109, 1998, 299-309.
Pflug G., Stochastic programs and statistical data, Annals of Operations Research, 84, 59-78, 1998.
Pflug G., Sampling derivatives of probabilities, Computing, 42, 315-328, 1989.
Pflug G., Applicational aspects of stochastic approximation, in Stochastic Approximation and Optimization of Random Systems, Eds. Ljung L., Pflug G., and Walk H., Birkhauser, Basel, 53-93, 1992.
Pflug G., On-line optimization of simulated Markovian processes, Math. Oper. Res., 15, 1990, 381-395.
Pflug G., Optimizing simulated systems, Simuletter, 15, 1984, 6-9.
Pflug G., and U. Dieter, (Editors), Simulation and Optimization, Lecture Notes in Economics and Math. Systems, 374, Springer-Verlag, 1992.
Phillis Y., V. Kouikoglou, D. Sourlas, and V. Manousiouthakis, Design of serial production systems using discrete event simulation and nonconvex programming techniques, International Journal of Production Research, 35, 1997, 753-766.
Pierreval H., Rule-based simulation metamodels, European J. of Operational Research, 61, 6-17, 1992.
Pierreval H., and J. Paris, From `simulation optimization' to `simulation configuration' of systems, Simulation Modelling Practice and Theory, 11, 5-19, 2003.
Pierreval H., and L.Tautou, Using evolutionary algorithm and simulation for the optimization of manufacturing systems, IIE Transactions, 29, 1997, 181-189.
Plambeck E., M. Fu, S. Robinson, and R. Suri, Sample-path optimization of convex stochastic performance functions, Mathematical Programming, 75, 1996, 137-176.
Polat S., and C. Bozda, Comparison of fuzzy and crisp systems via system dynamics simulation, European Journal of Operational Research, 138, 178-190, 2002.
Polyak B., New method of stochastic approximation type, Automation and Remote Control, 51, 1990, 937-946.
Polyak B., and A. Juditsky, Acceleration of stochastic approximation by averaging, SIAM Journal of Control and Optimization, 30, 1992, 838-855.
Pradlwarter H., and G Schueller, Assessment of low probability events of dynamical systems by controlled Monte Carlo simulation, Probabilistic Engineering Mechanics, 14, 213-227, 1998.

Radcliffe T., G. Barnea, B. Wowk, R. Rajapakshe, and S. Shalev, Monte Carlo optimization of metal/phosphor screens at megavoltage energies, Med. Phys., 20, 1993, 1161-1169.
Ramachandran K., V.Sivakumar, K. Sathiyanarayanan, and S. Chandraskekaran, Genetic based redundancy optimization, Journal of Microelectronics and Reliability, 37, 1997, 661-663.
Ramasesh R., and M. Jayakumar, Inclusion of flexibility benefits in discounted cash flow analyses for investment evaluation: A simulation/optimization model, Eur. J. Operational Research, 102, 1997, 124-141.
Rastrigin L., Extremal control by random search method scanning, Auto. and Remote Control, 21, 1960, 891-899.
Reiman M., B. Simon, and J. Willie, Simterpolations: Estimating an entire queueing function from a single sample path. Proceedings of the 1987 Winter Simulation Conference, 358-363, 1987.
Reiman M., and A. Weis, Sensitivity analysis for simulation via likelihood ratios, Operations Research, 37, 1986, 830-844.
Reiner G., Customer-oriented improvement and evaluation of supply chain processes supported by simulation models, International Journal of Production Economics, 96, 381-395, 2005.
Rezayat F., One the use of an SPSA-based model-free controller in quality improvement, Automatica, 31, 1995, 913-915.
Ricotti M., and E Zio, A neural network approach to sensitivity and uncertainty analysis, Reliability Engineering and System Safety, 64, 59-71, 1999.
Ridge J., S. Jones, M. Nielsen, and A. Shahani, Capacity planning for intensive care units, Eur. J. Operational Research, 105, 1998, 346-355,
Rief H., Monte Carlo Uncertainty Analysis, In CRC Handbook of Uncertainty Analysis, Edited by Y.Ronen, 1988
Robbins H., and S. Monro, A stochastic approximation method, Annals of Mathematical Statistics, 22, 1951, 400-407.
Robinson S., Analysis of sample-path optimization, Mathematics of Operations Research, 21, 1996, 513-528.
Robinson S., General concepts of quality for discrete-event simulation, European Journal of Operational Research, 138, 103-117, 2002.
Roberg P., and Ch. Abbess, Diagnosis and treatment of congestion in central urban areas, Eur. J. Operational Research, 104, 1998, 218-230.
Rocco C., S. Miller, J. Moreno, N. Carrasquero, and M. Medina, Sensitivity and uncertainty analysis in optimization programs using an evolutionary approach: A maintenance application, Reliability Engineering and System Safety, 67, 249-256, 2000.
Rodgers R., and A. Baddeley, Nested Monte Carlo study of random packing on the sphere, Journal of Applied Probability, 28, 1991, 539-552.
Rollans S. and D. McLeish, Estimating the optimum of a stochastic system using simulation, Journal of Statistical Computation and Simulation, 72, 357 - 377, 2002.
Romeijn H., and R. Smith, Simulated annealing for constrained optimization, Journal of Global Optimization, 5, 1994, 101-126.
Rosenblatt M., Y. Roll, and V. Zyse, A combined optimization and simulation approach for designing automated storage/retrieval systems, IIE Transactions, 25, 1993, 40-50.
Ross S., Variance reduction in simulation via random hazards, Probability in the Engineering and Informational Sciences, 4, 1990, 299-309.
Rossetti M., and G. Clark, Evaluating a queueing approximation for the machine interference problem with two types of stoppages via simulation optimization, Computers and Industrial Engineering, 34, 655-668, 1998.
Rousseau G., and K. Bauer Jr., Sensitivity analysis of a large-scale transportation simulation using design of experiments and factor analysis, in the Proceedings of the Winter Simulation conference, 1996.
Rubinstein R., How to optimize discrete-event systems from a single path by the score function method, Annals of Operations Research, 27, 1990, 175-212.
Rubinstein R., Modified importance sampling for performance evaluation and sensitivity analysis of computer simulation models, Mathematics and Computer in Simulation, 33, 1991, 1-22.
Rubinstein R., Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks, John Wiley, New York, 1986.
Rubinstein, R., Sensitivity Analysis and Performance Extrapolation for Computer Simulation Models. Operations Research, 37, 1989, 72-81.
Rubinstein R., The score function approach for sensitivity analysis of computer simulation models, Mathematics and Computer in Simulation, 28, 1986, 351-379.
Rubinstein R., Optimization of computer simulation models with rare events, European Journal of Operations Research, 99, 1997, 89-112.
Rubinstein R., Decomposable score function estimators for sensitivity analysis and optimization of queuing networks, Ann. Oper. Res., 39, 195-229, 1992.
Rubinstein R., Sensitivity analysis of discrete event systems by the ‘Push out' method, Ann. Oper. Res., 39, 229-251, 1992.
Ruppert D., A Newton-Raphson version of the multivariate Robbins-Monro procedure, Annals of Statistics, 13, 1985, 236-245.
Ruppert D., Almost sure approximations to the Robbins-Monro and Kiefer-Wolfowitz processes with dependent noise, Ann. Statist., 16, 1982, 178-187.
Ruppert D., R. Reish, R. Deriso, and R. Carroll, Monte-Carlo optimization by stochastic approximation, with application to harvesting of Atlantic menhaden, Biometrics, 40, 1984, 353-546.
Ruszczynski and W. Syski, Stochastic approximation method with gradient averaging for unconstrained problems, IEEE Trans. Auto. Control, AC-28, 1983, 1097-1105.

Sacks J., Asymptotic distribution of stochastic approximation procedure, Annals of Mathematical Statistics, 29, 1958, 397-398.
Sadegh P., Constrained optimization via stochastic approximation with simultaneous perturbation gradient approximation, Automatica, 33, 1997, 889-892.
Sadowski J., On the optimality and stability of exponential twisting in Monte Carlo simulation, IEEE Trans. Inform. Theory, IT-39, 119-128, 1993.
Sadowski J., and J. Bucklew, On deviations theory and asymptotically efficient Monte Carlo estimation, IEEE Trans. Inform. Theory, IT-36, 579-588, 1990.
Safizadeh M., Optimization in simulation: Current issues and the future outlook, Naval Research Logistics, 37, 1990, 807-825.
Safizadeh M., and R. Singnorile, Optimization of simulation via quasi-Newton method, ORSA Journal of Computing, 6, 1994, 398-408.
Safizadeh M., and B. Thorton, Optimization in simulation experiments using response surface methodology, Computers and Industrial Engineering, 8, 1984, 11-27.
Salzmann M., and F. Breitenecker, Genetic algorithms in discrete event simulation, Proceeding of the EUROSIM Conference, 213-218, 1995
Sanchez S., Sanchez P., Ramberg J., and F. Moeeni, Effective engineering design through simulation, Transactions in Operational Research, 3, 1997, 169-185.
Sanchez, S., L. Smith, and E. Lawrence, Sensitivity and scenario analyses for simulation metamodels, in the Proceedings of the Winter Simulation conference, 1996.
Sargent R., and T. Som, Current issues in frequency domain experimentation, Management Science, 38, 1992, 667-87.
Sattler H, A simulation analysis of brand investments, OR Spektrum, 22, 173-196, 2000.
Sauer N., and X. Xie, Marking Optimization of Stochastic Timed Event Graphs, In Lecture Notes in Computer Science, Vol. 691; Application and Theory of Petri Nets, 357-376, 1993.
Schmeiser B., and J. Wang, On the performance of pure adaptive search, Proceedings of the Winter Simulation Conference, 353-356, 1995.
Schruben L., Simulation optimization using frequency domain methods, in the Proceedings of the Winter Simulation conference, 366-369, 1986.
Schruben L., Simulation optimization using simultaneous replications and event time dilation, in the Proceedings of the Winter Simulation conference, 177-180, 1997.
Schruben, L., SIGMA: A graphical approach to teaching simulation, Journal of Computing in Higher Education, 4, 1992, 27-37.
Schruben L., A. Buss, and S. Jacobson, Driving frequency selection for frequency domain simulation experiments, Operations Research, 39, 1991, 917-24.
Schruben L., and V. Cogliano, An experimental procedure for simulation response surface model identification, Comm. ACM, 30, 1987, 716-730.
Schruben, L., and E. Yucesan, Structural and behavioral equivalence of simulation models, ACM Transactions on Modeling and Computation Simulation, 2, 1993, 82-103.
Scott E., Uncertainty and sensitivity studies of models of environmental systems, in the Proceedings of the Winter Simulation conference, 1996.
Scott M., and A. Saltelli, Editors, Special Issue on Sensitivity Analysis in the Journal of Statistical Computation and Simulations, 57(1-4), 1997.
Sexton R., R. Dorsey, and J. Johnson, Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing, Eur. J. Operational Research, 114, 1999, 589-601.
Shafer S., and T. Smunt, Empirical simulation studies in operations management: context, trends, and research opportunities, Journal of Operations Management, 22(4), 2004, 345-354.
Shahabuddin P., Importance sampling for the simulation of highly reliable Markovian systems, Management Science, 40, 1994, 333-52.
Shalmon M. and R. Rubinstein, Error analysis for regenerative queueing estimators with special reference to gradient estimators via likelihood ratio, Annals of Operations Research, 36, 383-396, 1992.
Shannon R., R. Mayer, and H. Adelsberger, Expert systems and simulation, Simulation, 44, 1985, 275-284.
Shao S., Percy P., and C. Yip, Rates of convergence of adaptive step-size of stochastic approximation Algorithms, Journal of Mathematical Analysis and Applications, 244, 333-347, 2000.
Shapiro A., Simulation-based optimization-convergence analysis and statistical inference, Communications in Statistics: Stochastic Models, 12, 1996, 425-435.
Shapiro A., A simulation-based approach to two-stage stochastic programming with recourse, Mathematical Programming, 81, 1998, 301-325.
Shapiro A., Simulation based optimization, in the Proceedings of the Winter Simulation conference, 1996.
Shapiro A., and Y. Wardi, Nondifferentiability of the steady-state function in discrete event dynamic systems, IEEE Transactions on Automatic Control, AC-39, 1994, 1707-1711.
Shapiro A., and Y. Wardi, Convergence analysis of stochastic algorithms, Mathematics of Operations Research, 21, 1996, 615-628.
Shapiro A., and Y. Wardi, Convergence analysis of gradient descent stochastic algorithms, Journal of Optimization Theory and Applications, 91, 1996, 439-454.
Sherman M., Batch variance estimators for the median of simulation output, Operations Research Letters, 23, 1998, 129-134
Shi L., and S. Olafsson, Nested partitions method for global optimization, Operations Research, 48, 390-407, 2000.
Shih N-H., The sensitivity analysis of binary networks via simulation, European Journal of Operational Research, 114, 602-609, 1999.
Shirish J., Sh. Hanif D., and T. Jeffrey, An enhanced response surface methodology (RSM) algorithm using gradient deflection and second-order search strategies, Computers and Operations Research, 25, 531-541, 1998.
Shonkwiler R.,and E. Van Vleck, Parallel dpeed-up of Monte Carlo methods for global optimization, Journal of Complexity, 10, 1994, 64-95.
Shorter J., and H. Rabitz, Risk analysis by the guided Monte Carlo technique, Journal of Statistical Computation and Simulation, 57, 1997, 321-336.
Siemiatkowski M., and W. Przybylski, Simulation studies of process flow with in-line part inspection in machining cells, Journal of Materials Processing Technology, 171(1), 2006, 27-34.
Sigman K., The stability of open queueing networks, Stoch. Proc. Appl., 35, 11-25, 1990.
Simon B., A new estimator of sensitivity measures for simulation based on light traffic theory, ORSA Journal on Computing, 1, 1989, 172-180.
Simpson T., J. Poplinski, P. Koch, and J. Allen, Metamodels for computer-based engineering design: Survey and recommendations, Engineering With Computers, 17, 129-150, 2001.
Srivastava J., Stochastic Simulation and Experimental Design Theory, Journal of Statistical Planning and Inference, Special Issue, 85 (1-2), 2000.
Smith D., An empirical investigation of optimum seeking in computer simulation, Operations Research, 21, 1973, 475-497.
Smith D., Requirements of an optimizer for computer simulation, Naval Res Logist. Quart., 20, 1973, 161-179.
Song W., A three-class variance swapping technique for simulation experiments, Operations Research Letters, 23, 63-70, 1999.
Spall J., Adaptive stochastic approximation by the simultaneous perturbation method, IEEE Transactions on Automatic Control, 45, 1839-1853, 2000.
Spall J., Estimation via Markov chain Monte Carlo, IEEE Control Systems Magazine, 23(2), 34-45, 2003.
Spall J., Implementation of the simultaneous perturbation algorithm for stochastic optimization, IEEE Transactions on Aerospace and Electronic Systems, 34, 817-823, 1998, (guidelines for practical implementation).
Spall J., A stochastic approximation technique for generating maximum likelihood parameter estimates, Proceedings of the American Control Conference, 1987, 1161-1167 (first paper on SPSA).
Spall J., A one-measurement form of simultaneous perturbation stochastic approximation, Automatica, 33, 1997, 109-112.
Spall J., Stochastic version of second-order (Newton-Raphson) optimization using only function measurements, in the Proceedings of the Winter Simulation conference, 1995.
Spall J., Accelerated second-order stochastic optimization using only function measurements, Proceedings of the 31st Conference on Information Sciences and Systems, 1997, 21-28.
Spall J., Multivariate stochastic approximation using a simultaneous perturbation gradient approximation, IEEE Transactions on Automatic Control , 37, 1992, 332-341.
Spall J., Developments in stochastic optimization algorithms with gradient approximations based on function measurements, Proceedings of the Winter Simulation Conference, 1994, 207-214 (review of several approaches in gradient-free setting).
Spendly W., G. Hext, and F. Himsworth, Sequential application of simplex designs in optimization and evolutionary operation, Technometrics, 4, 1962, 441-461.
Srichander R., Efficient schedules for simulated annealing, Engineering Optimization, 24, 1995, 161-176.
Srinivas M., and L. Patnaik, Genetic algorithms: A survey, Computer, 27(6), 1994.
Strickland S., Gradient/sensitivity estimation in discrete-event simulation, Proceedings of the Winter simulation conference, 1993, 97-105.
Stuckman B., and P. Stuckman, Design optimization using simulation and stochastic global search: A computer-aided engineering approach, Advances in Modelling and Simulation, 18, 1990, 13-33.
Sturgul J, Using exact statistical distributions for truck and shovel simulation studies, Surface Mining, 6, 1992, 137-39.
Sugihara K., A case study on tuning of genetic algorithms by using performance evaluation based on experimental design, In Proceedings of the 1997 Joint Conference on Information Sciences, 1997.
Sullivan D., and J. Wilson, Restricted subset selection procedure for simulation, Operations Research, 37, 1989, 52-71.
Suri R., Perturbation analysis: The state of the art and research issues explained via the GI/G/1 queue, Proceedings of IEEE, 77, 1989, 114-137.
Suri R., Infinitesimal perturbation analysis for general discrete event systems, Journal of the ACM, 34, 686-717, 1987.
Suri R., and B-R. Fu, On using continuous flow lines to model discrete production lines, Discrete Dynamic Systems, 4, 129-169, 1994.
Suri R. and M. Leung, Single run optimization of discrete event simulation: An empirical study using the M/M/1 queue, IIE Transactions, 21, 1989, 35-49.
Suri R. and M. Zazanis, Perturbation analysis gives strongly consistent sensitivity estimates for the M/G/1 queue, Management Sciences, 34, 1988, 39-64.
Swain J., S. Venkatraman, and J. Wilson, Distribution selection and validation, Journal of Statistical Computation and Simulation, 29, 1988, 271-297.
Swisher J., Hyden P., Jacobson Sh., and L. Schruben, A survey of recent advances in discrete input parameter discrete-event simulation optimization, IIE Transactions, 36, 591-600, 2004.

Tanaka K., Sato J., Guo J., Takada A., H. Yoshihara, A simulation model of hospital management based on cost accounting analysis according to disease, Journal of Medical Systems, 28(6), 2004, 689-710.
Tang Q., and H. Chen, Convergence of perturbation analysis based optimization algorithm with fixed number of customers period, Discrete Event Dynamic Systems: Theory and Applications, 4, 1994, 359-375.
Tarasenko G., Stochastic Optimization in the Soviet Union, Delphic Associates, Inc., Fall Church, VA., 1986 .
Teleb R., and F. Azadivar, A methodology for solving multi-objective simulation-optimization problems, European Journal of Operational Research, 27, 1994, 135-145.
Thanedar P., and G. Vanderplaats, Survey of discrete variable optimization for structural design, Journal of Structural Design, 121, 301-306, 1995.
Thompsona G., and J. Goodale, Variable employee productivity in workforce scheduling, European Journal of Operational Research, 170(2), 2006, 376-390.
Tofts C., Exact, analytic, and locally approximate solutions to discrete event-simulation problems, Simulation Practice and Theory, 6, 721-759, 1998.
Tokal G., D. Goldsman, D. Ockerman, and J. Swain, Standardized time series L_p-norm variance estimators for simulation, Management Science, 44, 1998, 234-245.
Tompkins G., and F. Azadivar, Genetic algorithms in optimizing simulated systems, Proceedings of the Winter Simulation Conference, 757-762, 1995.
Torn A., and A. Zilinskas, Global Optimization, Springer-Verlag, 1989.
Tornamobe A., Discrete-Event System Theory, World Scientific, London, 1995.
Traub J., and H. Wozniakowski, The Monte Carlo algorithm with a pseudo-random generator, Mathematics of Computation, 58, 1992, 303-339.
Tsai C-Sh., Evaluation and optimisation of integrated manufacturing system operations using Taguch's experiment design in computer simulation, Computers And Industrial Engineering, 43, 591-604, 2002.
Tsinias J., The concept of `Exponential input to state stability' for stochastic systems and applications to feedback stabilization, Systems & Control Letters, 36, 221-229, 1999.
Tsoucas P., Rare events in series of queues, J. Appl. Probab., 29, 168-175, 1992.
Tunali S., and I. Batmaz, Dealing with the least squares regression assumptions in simulation metamodeling, Computers & Industrial Engineering, 38, 307-320, 2000.

Ulrich E., Agrawal V., and J. Arabian, Concurrent and Comparative Discrete Event Simulation, Kluwer Academic, Boston, 1994.
Urayas'ev S. Derivatives of probability functions and integrals over sets given by inequalities, J. Comput. Appl. Math., 56, 197-223, 1994.
Uryasev, S., Analytic perturbation analysis for DEDS with discontinuous sample-path function, Communications in Statistics-Stochastic Models, 13, 1997, 457-490.

Van Groenendaal W., and J. Kleijnen, Deterministic versus stochastic sensitivity analysis in investment problems: An environmental case study, European Journal of Operational Research, 141, 8-20, 2002.
Vassiliou P., The perturbed nonhomogeneous Markov systems, Linear Algebra and Its Applications, 289, 319-332, 1999.
Vazquez-Abad F., Sensitivity analysis for stochastic DEDS: An overview, Aportaciones Matematicas, Notas de Investigacion, 7, 1992, 163-182.
Vázquez-Abad F., Strong points of weak convergence: A study using RPA gradient estimation for automatic learning', Automatica, 35, 1999, 1255-1274.
Vázquez-Abad F., C. Cassandras, and V. Julka, Centralized and decentralized asynchronous optimization of stochastic discrete event systems, IEEE Transactions on Automatic Control, 43, 1998, 631-655.
Vazquez-Abad F., and P. L'Ecuyer, Simulation trees for functional estimation via the phantom method, Lecture Notes in Control and Information Sciences, 199, 1994.
Vazquez-Abad F., and P. LeQuoc, Sensitivity analysis for ruin probabilities: Canonical risk model, Journal of the Operational Research Society, 52, 71-81, 2001.
Venter J., An extension of the Robbins-Monro procedure, Annals of Statistics, 15, 1967, 1115-1130.
Veral E., Computer simulation of due-date setting in multi-machine job shops, Computers & Industrial Engineering, 41, 77-94, 2001.
Visipkov V., Y. Merkuryev, and L. Rastrigin, Optimization of discrete system simulation models(Survey), Automatic Control and Computer Sciences, 28, 1994, 10-20.
Vonk Noordegraaf A., M. Nielen, and J. Kleijnen, Sensitivity analysis by experimental design and metamodelling: Case study on simulation in national animal disease control, European Journal of Operational Research, 146, 433-443, 2003.

Walk H., Foundations of stochastic approximation, in Stochastic Approximation and Optimization of Random Systems, Eds. by Ljung L., Pflug G. and Walk H., Birkhauser, Basel, 2-51, 1992.
Wang H., and Pham H., Survey of reliability and availability evaluation of complex networks using Monte Carlo techniques, Microelectronics and Reliability, 37, 187-209, 1997.
Wang J., Distribution sensitivity analysis for stochastic programs with complete recourse., Math. Program., 31, 1985, 286-297.
Wang, J., Contributions to Monte Carlo Analysis: Variance Reduction, Random Search, and Bayesian Robustness, Ph.D. Dissertation, Purdue University, 1994.
Wang J., and J. Chen, On the strong consistency of the maximum likelihood estimators from randomly censored samples, International Journal of Reliability, Quality and Safety Engineering, 4, 35-53, 1997.
Wang P., and D. Chin, Continuous optimization by a variant of simulated annealing, Computational Optimization and Applications, 6, 1996, 59-71.
Wang T., H. Lin, and K. Wu, An improved simulated annealing for facility layout problems in cellular manufacturing systems, Computers and Industrial Engineering, 34, 1998, 309-319.
Wang T., and K. Wu, A parameter set design procedure for the simulated annealing algorithm under the computational time constraint, Computers & Operations Research, 26, 665-678, 1999.
Wardi Y., Stochastic algorithms with Armijo stepsizes for minimization of functions, Journal of Optimization Theory and Applications, 64, 1990, 399-417.
Wardi Y., and J-Q. Hu, Strong consistency of infinitesimal perturbation analysis for tandem queueing networks, J. Discrete Event Dynamic Systems, 1, 1991, 37-59.
Wardi Y., M. Kellmans, C. Cassandras, and W. Gong, Smoothed perturbation analysis algorithms for estimating the derivatives of occupancy-related functions in serial queueing networks, Ann. of Operations Research, 39, 269-295, 1992.
Watson E., P. Chawda, B. McCarthy, M. Drevna, and R. Sadowski, A simulation metamodel for response-time planning, Decision Sciences, 29, 1998, 217-241.
Wee, I.-S., Stability for multidimensional jump-diffusion processes, Stochastic Processes and their Applications, 80, 193-209, 1999.
Wei C., Multivariate adaptive stochastic approximation, Annals of Statistics, 15, 1987, 1115-1130.
Welch S., and S. Salhi, The obnoxious p facility network location problem with facility interaction, Eur. J. Operational Research, 102, 1997, 302-319.
Wets R., Statistical estimation from an optimization viewpoint, Annals of Operations Research, 84, 79-102, 1998.
Wiendahl H., and J. Worbs, Simulation based analysis of complex production systems with methods of non-linear dynamics, Journal of Materials Processing Technology, 139(1-3), 2003, 28-34.
Whitt W., The efficiency of one long run versus independent replications in steady-state simulation, Management Science, 37, 1991, 645-66.
Whitt W., Planning Queueing Simulations, Management Science, 35, 1989, 1341-1366.
Whitt W., Minimizing delays in the GI/G/1 queue, Operations Research, 32, 41-51, 1984.
Wild R., and J. Jr. Pignatiello, Finding stable system designs: A reverse simulation technique, Communications of the ACM, 35, 87-98, 1994.
Wild R., and J. Jr. Pignatiello, An experimental design strategy for designing robust systems using discrete-event simulation, Simulation, 53, 1991, 358-368.
Wilde D., and C. Beightler, Foundations of Optimization, Prentice-Hall, Englewood Cliffs, NJ., 1967.
Williams T., Towards realism in network simulation, Omega, 27, 305-314, 1999.

Xiao N., F. Wu, and S. Lun, Dynamic bandwidth allocation using infinitesimal perturbation analysis, IEEE Infocom ' 94, 383-389, 1994.
Xie X., Dynamics and convergence rate of ordinal comparison of stochastic discrete-event systems, IEEE Transactions on Automatic Control, 42, 586-590, 1997.

Yakowitz S., A globally convergent stochastic approximation, SIAM Journal on Control and Optimization, 31, 30-40, 1993.
Yakowitz S., T. Jayawaaden, and S. Li, Theory for automatic learning under partially observed Markov-dependent noise, IEEE Trans. Auto. Control, AC-37, 1992, 2316-1324.
Yan D., and H. Mukai, Stochastic discrete optimization, SIAM Journal on Control and Optimization, 30, 1990, 594-612.
Yan H., and X. Zhou, Finding optimal number of kanbans in a manufacturing system via perturbation analysis, Lecture Notes in Control and Information Sciences, 199, Springer-Verlag, 572-578, 1994..
Yang J., and H. Kushner, A Monte Carlo method for sensitivity analysis and parametric optimization of nonlinear stochastic systems, SIAM J. Control and Optimization, 29, 1216-1249, 1991.
Yang J-M., and J-H. Kim, Optimization of discrete event systems using evolutionary programming, Proceedings of 1996 IEEE International Conference on Evolutionary Computation, 131-134, 1996.
Yang K-Kh., C-C. Sum, An evaluation of due date, resource allocation, project release, and activity scheduling rules in a multiproject environment, Eur. J. Operational Research, 103, 1997, 139-154.
Yang W-N., and B. Nelson, Multivariate batch means and control variates, Management Science, 38, 1992, 1415-31.
Yang W-N., and B. Nelson, Using common random numbers and control variates in multiple-comparison procedures, Operations Research , 39, 1991, 583-591.
Yao J., On constrained simulation and optimization by Metropolis chains, Statistics and Probability Letters, 46, 187-193, 1999.
Yin G., On extensions of Polyak's averaging approach to stochastic approximation, Stochastics And Stochastics Reports, 36, 1991, 245-264.
Yin G., and Y. Zhu, Almost sure convergence of stochastic approximation algorithms with non-additive noise, International Journal of Control, 49, 1989, 1361-1376.
Yoo J., and P. Hajela, Immune network simulations in multicriterion design, Structural Optimization, 18, 85-94, 1999.
Yoon A., Randomized algorithms and global optimization for optimal ad robust control, Doctoral Dissertation, The University of Michigan, 1998.
Yu B., and K. Popplewell, Metamodels in manufacturing: A review, Int. J. Prod. Res., 32, 1994, 787-796.
Yuan M., and B. Nelson, Multiple comparisons with the best for steady-state simulation, ACM Transactions on Modeling and Computer Simulation, 3, 66-79, 1993.
Yucesan E., and S. Jacobson, The complexity of rapid learning in discrete event simlulation, IIE Transactions, 29, 1997, 783-790.
Yucesan E., Y.-C. Luo, C.-H. Chen, and I. Lee, Distributed web-based simulation experiments for optimization, Simulation Practice and Theory, 9, 73-90, 2001.
Yunker J., The Optimization of Simulation Models by Genetic Algorithms: A Comparative Study, Ph.D. dissertation, Virginia Polytechnic Institute and State University. 1993.
Yunker J., and J. Tew, Simulation optimization search, Journal of Mathematics and Computers in Simulation, 37, 1994, 17-28.

Zabinsky Z., and R. Smith, Pure adaptive random search in global optimization, Mathematical Programming, 53, 1992, 323-338.
Zee D.J. van der, Modeling decision making and control in manufacturing simulation, International Journal of Production Economics, 100(1), 2006, 155-167.
Zhang and Y. Ho, Performance gradient estimation for very large finite Markov chains, IEEE Trans. Auto. Control, AC- 36, 1991, 1218-1227.
Zhang J. and, X. Xu, An efficient evolutionary programming algorithm, Computers & Operations Research, 26, 645-663, 1999.
Zhigljavsky A., Theory of Global Random Search, Kluwer Academic Publisher, Boston, Massachusset, 1991.
Zülch G, U. Jonsson, and J. Fischer, Hierarchical simulation of complex production systems by coupling of models, International Journal of Production Economics, 77(1), 2002, 39-51.
Zülch G., S. Rottinger, and T. Vollstedt, A simulation approach for planning and re-assigning of personnel in manufacturing, International Journal of Production Economics, 90(2), 2004, 265-277.

Books: Authors' Index

A B C D E F G H I

J K L M N O P Q R

S T U V W X Y Z

Aburdene M., Computer Simulation of Dynamic Systems, Wm. C. Brown, 1988.
Ahrweiler P., and N. Gilbert, (Eds.), Computer Simulations in Science and Technology Studies, Springer, 1987.

Arsham H., J. Kreimer, and R. Rubinstein, Application of Radon-Nikodym theorem for simulation of queueing system, Discrete Event Simulation and Operations Research, SCS Publication, Belgium, 95-99, 1987.
Asmussen S., Ruin Probabilities, World Scientific Press, 2000.

Balemi S., P. Kozak, and R. Smedinga, (eds.), Discrete Event Systems: Modeling and Control, Birkhauser, Basel, 1993.
Banks J., (Ed.), Handbook of Simulation, John Wiley, 1998.
Banks J., Carson J., and B. Nelson, Discrete-Event System Simulation, Prentice Hall, 2000.
Bechhofer R., T. Santer, and D. Goldsman, Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons, Wiley, 1995.
Benveniste A., M. Metivier, and P. Priouret, Adaptive Algorithms and Stochastic Approximations, Springer-Verlag, New York, 1990.
Bequette B., Process Dynamics; Modeling, Analysis and Simulation, Prentice Hall, 1997.
Binder K., and D. Heermann, Monte Carlo Simulation in Statistical Physics: An Introduction, Springer Verlag, 1998.
Birge J, and F. Louveaux, Introduction to Stochastic Programming, Springer, New York, 1997.
Bolch G., S. Greiner, H. de Meer, and K. Trivedi, Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications, Wiley, 1998.
Performance analysis seeks to discover the information bottlenecks in a computer system, and allows the system designer to create an optimal system for a specific need. This book presents a self-contained and complete presentation of the theory and application of computer performance evaluation based on queueing theory and Markov chains. After beginning with basic probability theory, Queueing Networks and Markov Chains proceeds to the more complicated topics of queueing networks and Markov chains, using applications and examples to illustrate key points.
Bossel H., Modeling & Simulation, A. K. Peters Pub., 1994.
Bouleau N. and D. Lepingle, Numerical Methods for Stochastic Processes, John Wiley, 1994.
Bennett B., Simulation Fundamentals, Prentice Hall, 1995.
Birtwistle G., DEMOS: Discrete Event Modelling on Simula, MacMillan, 1979.
Bratley P., B. Fox and L. Schrage, A Guide to Simulation, Springer-Verlag, New York, 1983.
Bucklew J., Large Deviation Techniques in Decision, Simulation and Estimation, Wiley, 1990.
Bulgren, W., Discrete System Simulation, Prentice-Hall, 1982

Cairoli R., R. and Dalang Sequential Stochastic Optimization, Wiley, 1995.
Cassandras C., Discrete Event Systems: Modeling and Performance Analysis, Irwin, Boston, MA, 1993.
Cassandras C., Rapid Learning Techniques for Discrete Event Systems: Modeling and Performance Analysis, Irwin, 1993.
Cassandras C., and S. Lafortune, Introduction to Discrete Event Systems, Kluwer Academic Pub., 1999.
Cellier F., Continuous System Modeling, Springer Verlag, 1991.
Checkland P., Systems Thinking, Systems Practice: Includes a 30-Year Retrospective, Wiley, 1999.
Chen M-H., Q-M. Shao, and J. Ibrahim, Monte Carlo Methods in Bayesian Computation, Springer, 2000.
Chorafas D., Simulation, Optimization, and Expert Systems, Probus Pub Co., 1991.
Clymer J., Systems Analysis Using Simulation and Markov Models, Prentice Hall, 1990.
Coyle R., System Dynamics Modelling, Chapman & Hall, London, 1996.
Curry G., et al., Discrete Simulation: Fundamental and Microcomputer Support, Holden Day, 1989

Dagpunar J., Principles of Random Variate Generation, Clarendon Press, Oxford, 1988.
De Cogan D. and A. De Cogan, Applied Numerical Modelling For Engineers, Oxford University Press, 1997.

Elzas M., T. Oren, and B. Zeigler (eds.), Modelling and Simulation Methodology: Knowledge Systems Paradigms, North-Holland, 1989.
Ermakov, S., and V. Melas, Design and Analysis of Simulation Experiments, Kluwer, Boston, 1995.
Ertas A., and J. Jones, The Engineering Design Process, Wiley, 1997.
Evans J., Structures of Discrete Event Simulation: An Introduction to the Engagement Strategy, Ellis Horwood ; Chichester, Halsted Press, New York, 1988.
Evans J., and D. Olson, Introduction to Simulation and Risk Analysis, Prentice Hall, 2002.

FeldmanPh., Discrete-Event Simulation for Performance Evaluation Systems With Algorithms and Example in C and C++, Wiley, 2000.
Fiedler B., (ed.), Handbook of Dynamical Systems, Elsevier Science, 2002.
Fishman G., Discrete-Event Simulation: Modeling, Programming and Analysis, Springer-Verlag, Berlin, 2001.
Fishman G., Monte Carlo: Concepts, Algorithms and Applications, Springer-Verlag, New York, 1996.
Fishman G., Principles of Discrete Event Simulation, Wiley, 1978
Fishman G., Concepts and Methods in Discrete Event Digital simulation, Wiley, 1973
Fishwick P., Simulation Model Design and Execution: Building Digital Worlds, Prentice-Hall, Englewood Cliffs, 1995.
Fu M., and J.Q. Hu, Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic Pub., 1997.
Fu M., and J-Q. Hu, Conditional Monte Carlo: Gradient Estimation and Optimization Applications, Kluwer Academic Publishers, 1997.

Gamerman D., Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, CRC Press, 1997.
Gardner F., and J. Baker, Simulation Techniques, Two Volumes, Wiley, London, 1996.
Ghosh S., and T. Lee, Modeling & Asynchronous Distributed Simulation: Analyzing Complex Systems, IEEE Publications, 2000.
Gilbert G., and K. Troitzsch, Simulation for the Social Scientist, Open Univ. Press, 1999.
Gimblett R., Integrating Geographic Information Systems and Agent-Based Modeling: Techniques for Simulating Social and Ecological Processes, Oxford University Press, 2002.
Glasserman P., Gradient Estimation via Perturbation Analysis, Kluwer, Boston, 1991.
Goldberg D., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1994.
Gosavi A., Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, Kluwer Academic Publishers, 2003. It provides the foundation simulation-based optimization techniques and their computational implementation aspects.
Gould H., Introduction to Computer Simulation Methods, Addison Wesley Pub. Co., 1995.

Haines S., The Systems Thinking Approach to Strategic Planning and Management, CRC Press, 2000.
Hamilton, J., D. Nash, and U. Pooch, (eds.), Distributed Simulation, CRC Press, 1997.
Hammersley J., and D. Handscomb, Monte Carlo Methods, Chapman and Hall, London, 1964.
Harrell Ch, and K. Tumay, Simulation Made Easy: A Manager's Guide, Inst. of Industrial Engineers, 1996.
Harrington J., and K. Tumay, Simulation Modeling Methods: An Interactive Guide to Results-Based Decision, McGraw-Hill, 1998.
Harrington J., and K. Tumay, Simulation Modeling Methods: To Reduce Risks and Increase Performance, McGraw-Hill, 2000. CD-ROM included.
Haas P., Stochastic Petri Net Models Modeling and Simulation, Springer Verlag, 2002.
Harrington H. J., and K. Tumay, Simulation Modeling Methods: To Reduce Risks and Increase Performance, McGraw-Hill; 2000.
Hauge J., and K. Paige, Learning SIMUL8: The Complete Guide, NovaSim Company, Bellingham, WA, 2001.
Haverkort B., Performance of Computer-Communication Systems, John Wiley and Sons, 1998.
Hill D., Object-Oriented Analysis and Simulation Modeling, Addison-Wesley, 1996.
Hishman G., Principles of Discrete Event Simulation, John Wiley, New York, 1978
Ho Y., Discrete Event Dynamic Systems, IEEE press, 1992.
Ho Y. and X. Cao, Perturbation Analysis of Discrete Event Dynamic Systems, Kluwer, Norwell, Massachusetts, 1991.
Ho Y., and C. Cassandras, Perturbation analysis for control and optimization of queueing systems: An overview and the state of the art, in Frontiers in Queueing, J. Dshalalow, Ed., CRC Press, 1997.
Hoover S., and R. Perry, Simulation: A Problem-Solving Approach, Addison Wesley, 1989
Hoppensteadt F., and Ch. Peskin, Modeling and Simulation in Medicine and the Life Sciences, Springer-Verlag, 2002. It uses simple examples in which elementary mathematical models can be used to gain useful biological insight.
Jain R., The Art of Computer Systems Performance Analysis Techniques for Experimental Design, Measurement, Simulation, and Modeling, Wiley, 1991.
Jeruchim M., Ph. Balaban, and K. Shanmugan, (eds.), Simulation of Communication Systems: Modeling, Methodology, and Techniques, Plenum Pub Corp., 2001.

Karian Z., and E. Dudewicz, Modern Statistical Systems and GPSS Simulation, CRC Press, 1998.
Kelton W., R. Sadowski, and D. Sadowski, Simulation with Arena, McGraw-Hill, 1998.
Khoshnevis B., Discrete Systems Simulation, McGraw-Hill, New York, 1994
Kitamura R. and M. Kuwahara, (Eds.), Simulation Approaches in Transportation Analysis, Springer, 2005.
Kleijnen J., Statistical Techniques in Simulation, Parts I and II, Dekker, New York, 1974.
Kleijnen J., Statistical Tools for Simulation Practitioners, Marcel Dekker, 1987.
Kleijnen J., and W. van Groenendaal, Simulation: A Statistical Perspective, Wiley, Chichester, 1992
Kleinrock L., Queueing Systems, Vol 1 & 2, Wiley, 1975.
Knepell P., Simulation Validation and Confidence Assessment Methods, IEEE COMP SOC., 1993.
Knuth D., The Art of Computer Programming: Seminumerical Algorithms, Addison Wesley Pub., 1969.
Kochenburger R., Computer Simulation of Dynamic Systems, Prentice Hall, 1972.
Kouikoglou V., and Y. Phillis, Hybrid Simulation Models of Production Networks, Kluwer Pub., 2001.
Kreutzer W., System Simulation: Programming Styles & Languages, Addison Wesley, 1986.
Kuipers B., Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge, MIT Press, 1994.
Kulkarni V., Modeling and Analysis of Stochastic Systems, Chapman & Hall, 1995.
Kumar P., and P. Varaiya, (eds.), Discrete Event Systems, Manufacturing Systems, and Communication Networks, Springer Verlag, 1995.

Law A., and W. Kelton, Simulation Modeling and Analysis, McGraw-Hill, 2000. To get the programs and data, use ftp://msi.umn.edu/pub/dkelton/lkbook
Lewis P., and E. Orav, Simulation Methodology for Statisticians, Operations Analysts, and Engineers, Wadsworth Inc., 1989
Lindemann Ch., Performance Modelling with Deterministic and Stochastic Petri Nets, John Wiley & Sons 1998.
Ljung L., G. Pflug, and H. Walk, Stochastic Approximation and Optimization of Random Systems, Birkhauser, 1992.

Madu Ch., and Ch-H. Kuei, Experimental Statistical Designs and Analysis in Simulation Modeling, Greenwood Publishing Group, 1993.
Marek P., Simulation Based Reliability Assessment, CRC PRESS, INC, 1996.
Matloff N., Probability Modelling and Computer Simulation, PWS-Kent Pub. Co., 1988.
Maurice A.,Computer Simulation of Dynamic Systems, Dubuque, Iowa, 1988.
McHaney R., Computer Simulation: A Practical Perspective, Academic Press, 1991.
McLeish Don L., Monte Carlo Simulation and Finance, John Wiley & Sons, 2005.
Mitrani I., Simulation Techniques for Discrete Event Systems, Cambridge University Press, 1982
Mikhailov G., New Monte Carlo Methods with Estimating Derivatives, VSP BV, Zeist, The Netherlands, 1995.
Morgan B., Elements of Simulation, Chapman and Hall, New York, 1984.

Neelamkavil F., Computer Simulation and Modelling, Wiley, 1987.
Nelson B., Stochastic Modeling: Analysis & Simulation, McGraw-Hill, 1995.
Nersesian R., Computer Simulation in Business Decision Making: A Guide for Managers, Planners, and MIS Professionals, Quorum Books, New York, 1989.
Noreen E., Computer Intensive Methods for Testing Hypotheses: An Introduction, Wiley, 1989.
Oakshott L., Business Modelling and Simulation, Pitman Publishing, London, 1997.
Pandu R. Tadikamalla P., (ed.), Modern Digital Simulation Methodology: Input, Modeling, and Output, Amer. Sciences Pr., 1985.
Passino K., and K. Burgess, Stability Analysis of Discrete Event Systems, Wiley, 1998.
Pegden C., Introduction to Simulation Using SIMAN, McGrow Hill, 1995.
Pegden, P., Computer Simulation/Verification: Introduction to Simulation Using SIMAN, WCB/McGraw-Hill, 1995.
Pidd M., (ed.), Computer Modelling for Discrete Simulation, Wiley 1989.
Pidd M., Tools for Thinking: Modelling in Management Science, Wiley, 1997.
Pidd M., Computer Simulation in Management Science, Wiley, 1998.
Pollatschek M., Programming Discrete Simulations, Publishers Group West, 1996.
Pooch U., and J. Wall, Discrete Event Simulation: A Practical Approach, CRC Press, 1993.
Pritsker A., The Gasp IV Simulation Language, Wiley, 1974. Discrete-continuous simulation system written in Fortran.
Pritsker A., Introduction to Simulation & SLAM II, Wiley, 1995.
Pritsker A., J. O'Reilly, and D. LaVal, Simulation with Visual SLAM and AweSim, Wiley, 1997.
Profozich d., Managing Change with Business Process Simulation, Prentice Hall, 1997.

Randers J., Elements of the System Dynamics Method, Productivity Press, 1980.
Reitman J., Computer Simulation Applications: Discrete-Event Simulation for Synthesis and Analysis of Complex Systems, Krieger Publishing Co., 1981.
Ripley B.Stochastic Simulation, Wiley, 1987.
Robert C., and G. Casella, Monte Carlo Statistical Methods, Springer, 1999.
Roberts N., D. Anderson, and R. Deal, Introduction to Computer Simulation: Systems Dynamics Modeling Approach, Productivity Press, 1997.
Robinson S., Successful Simulation: A Practical Approach to Simulation Projects, McGraw-Hill, 1994.
Romanowicz B., Methodology for the Modeling and Simulation of Microsystems, Kluwer Academic Publishers, 1998.
Ross Sh., A Course in Simulation, Macmillan, 1990.
Ross Sh., Simulation, Academic Press, 1997.
Rubinstein R., Simulation and The Monte Carlo Method, Wiley, 1981.
Rubinstein R., Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks, Wiley, 1986.
Rubinstein R., and B. Melamed, Modern Simulation and Modeling, Wiley, 1998.
Rubinstein R., and A. Shapiro, Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization, Wiley, 1993.

Sadiku M., and M. Ilyas, Simulation of Local Area Networks, CRC Press, 1995.
Sarjoughian H., and F. Cellier (eds.), Discrete Event Modeling and Simulation: Enabling Future Technologies, Springer Verlag, 2000.
Saviotti P., (Ed.), Applied Evolutionary Economics: New Empirical Methods and Simulation Techniques, Edward Elgar Pub., 2002.
Sauer Ch., Simulation of Computer Communication Systems, Prentice-Hall, 1983.
Schriber T., An Introduction to Simulation Using GPSS/H, Wiley, 1991.
Severance F., System Modeling and Simulation: An Introduction, Wiley, 2001.
Shanbhag D., and C. Rao, (eds.), Stochastic Processes: Modeling and Simulation, Elsevier, 2003.
Shannon R., Systems Simulation: The Art and Science, Prentice-Hall, 1975
Simscript II.5 Reference Handbook, Consolidated Analysis Centers Inc., Los Angeles, CA, 1972.
Sobol´ I., A Primer for the Monte Carlo Method, CRC Press, 1994.
Spall J., Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiley, 2003. A unique interdisciplinary foundation for real-world problem solving, covering a broad range of today’s most widely used stochastic algorithms, including: Random search, Stochastic approximation, Genetic and evolutionary methods, Model selection, and Markov chain Monte Carlo. Information and supporting materials are available at www.jhuapl.edu/ISSO.
Stender J., and E. Hillebrand, (eds.), Genetic Algorithms in Optimisation, Simulation and Modelling, IOS Press, 1994.

Tezuka Sh., Uniform Random Numbers: Theory and Practice, Kluwer Academic Publishers, 1995.
Thesen A., and L. Travis, Simulation for Decision Making, PWS Publishing Company, 1995.
Thompson J., and R. Tapia, Nonparametric Function Estimation, Modeling & Simulation, Siam, 1990.
Tornambe A., Discrete-event System Theory: An Introduction, World Scientific, 1995.
Trivedi, K. S., Probability and Statistics with Reliability, Queueing, and Computer Science Applications, 2^nd edition, John Wiley, 2001.

Ulrich E., V. Agrawal, and J. Arabian, Concurrent and Comparative Discrete Event Simulation, Kluwer Academic, 1994

Van den Bosch, P. and A. Van der Klauw, Modeling, Identification & Simulation of Dynamical Systems, CRC Press, 1994.
Viswanadham N., and Y. Narahari, Performance Modeling of Automated Manufacturing Systems, Prenticel Hall, 1992
Vose D., Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling, Wiley, Chichester, 1996.
Vose D., Risk Analysis: A Quantitative Guide, John Wiley & Sons, 2000.

Warson H., Computer Simulation in Business, Wiley, 1981.
Watkins K., Discrete Event Simulation in C, McGraw-Hill, 1994.
Watson H., and J. Blackstone, Jr., Computer Simulation, Wiley, 1989.
Weinberg G., An Introduction to General Systems Thinking, Dorset House, 2001.
Wilson W., Simulating Ecological and Evolutionary Systems in C, Cambridge University Press, 2000.
Winston W., Financial Models Using Simulation and Optimization, Palisade Corporation, 1998.
Winston W., Simulation Modeling Using @RISK, ITP, 1996.
Woods R., and K. Lawrence, Modeling and Simulation of Dynamic Systems, Prentice Hall, 1997.
Yao D., Zhang H., and X.. Zhou, (Eds.), Stochastic Modeling and Optimization, Springer, 2003.
Yin G., and Q. Zhang, (Eds.), Mathematics of Stochastic Manufacturing Systems, American Mathematical Society, 1997.

Zeigler B., Theory of Modelling and Simulation, Wiley, 1976.
Zeigler B., Multifaceted Modelling and Discrete Event Simulation, Academic Press, 1984
Zeigler B., Object Oriented Simulation With Hierarchical Modular Models: Intelligent Agents and Endomorphic Systems, 1990.
Zeigler B., H. Praehofer, and T-G. Kim, Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems, Academic Press, 2000.
Zobrist G. and Leonard J. (eds), Progress in Simulation, Volumes I and II, Ablex Publishing,Norwood, NJ., 1995.

Additional Books and Journal Articles: Authors' Index

A B C D E F G H I

J K L M N O P Q R

S T U V W X Y Z

Aazhang B., and J.R Cavallaro, Multitier wireless communications, Wireless Personal Communications, 17, 323-330, 2001.
Abbott Catherine A., Michael W. Berry, E.Jane Comiskey, Louis J. Gross, and Hank-Kwang Luh, Parallel individual-based modeling of Everglades deer ecology, IEEE Computational Science & Engineering, 4, 60-72, 1997.
Aboelela E., and M. Kaufmann, Aboelela Network Simulation Experiments Manual, The Morgan Kaufmann Series in Networking, 2003.
Ahmed M., T. Alkhamis, and M. Hasan, Optimizing discrete stochastic systems using simulated annealing and simulation, Computers and Industrial Engineering, 32 (4), 823-836, 1997.
Ajami N.K., Q. Duan, , X. Gao, and S. Sorooshian, Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results, Journal of Hydrometeorology, 7 (4), 755-768, 2006.
Ali M., A.To¨rn, and S. Viitanen, A direct search variant of the simulated annealing algorithm for optimization involving continuous variables, Computers and Operations Research, 29, 87-102, 2002.
Al-Sultan K., A tabu search Hooke and Jeeves algorithm for unconstrained optimization, European Journal of Operational Research, 103 (2), 198-208, 1997.
Anderson N.P., G.W. Evans, and W.E. Biles, Simulation optimization of logistics systems through the use of variance reduction techniques and criterion models, Engineering Optimization, 38 (4), 441-460, 2006.
Andjelkovic´ B., V. Litovski, and V. Zerbe, Mission level modeling and simulation language for mixed-signal system-on-chip design, Journal of Circuits, Systems and Computers, 16 (1), 15-28, 2007.
Andradottir S., Optimization of transient and steady-state behavior of discrete event systems, Management Science, 42 (3), 717-737, 1996.
Andradottir S., Simulation optimization, Handbook on Simulation, 307-33, 1998.
Andradottir S., A scaled stochastic approximation algorithm, Management Science, 42 (2), 475-498, 1996.
Aqel M.M., A simulation technique for engineering control systems, Journal of Applied Sciences, 6 (1), 157-162, 2006.
Argyris J., M. De Donno, and F.L. Litvin, Computer program in Visual Basic language for simulation of meshing and contact of gear drives and its application for design of worm gear drive, Computer Methods in Applied Mechanics and Engineering, 189 (2), 595-612, 2000.
Asdre K., and S. Nikolopoulos, P-tree structures and event horizon: Efficient event-set implementations, Journal of Computer Science and Technology, 21, 19-26, 2006.
Avramidis A., and J. Wilson, Correlation-induction techniques for estimating quantiles in simulation experiments, Operations Research, 46, 574-591, 1998.
Ayres M., D. Wait, T. Le, and M. Wiederholt, Simulation of large scale, spacecraft power systems using sparse-matrix solution techniques, Advances in Engineering Software, 31, 593-598, 2000.
Azadiva F., and Y. Lee, Optimization of discrete variable stochastic systems by computer simulation, Mathematics and Computers in Simulation, 30 (2), 331-345, 1988.

Bäck T., and H. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, 1 (1),1-23, 1993.
Baeza, A., and P. Mulet, Adaptive mesh refinement techniques for high-order shock capturing schemes for multi-dimensional hydrodynamic simulations, International Journal for Numerical Methods in Fluids, 52 (4), 455-471, 2006.
Bagrodia R.L., and M. Takai, Performance evaluation of conservative algorithms in parallel simulation languages, IEEE Transactions on Parallel and Distributed Systems, 11 (4), 395-411, 2000.
Bahloul R., A. Mkaddem, Ph. Dal Santo, and A. Potiron, Sheet metal bending optimisation using response surface method, numerical simulation and design of experiments, International Journal of Mechanical Sciences, 48, 991-1003, 2006.
Balevic?ius R., A. Dz?iugys, R. Kac?ianauskas, A. Maknickas, and K. Vislavic?ius, Investigation of performance of programming approaches and languages used for numerical simulation of granular material by the discrete element method, Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, 1998.
Barr R., Z. Haas, and R. Van Renesse, JiST: An efficient approach to simulation using virtual machines, Software - Practice and Experience, 35, 539-576, 2005.
Barton R. , and J. Ivey, Jr., Nelder-Mead simplex modifications for simulation optimization, Management Science, 42 (7), 954-973, 1996.
Batmaz I., and S. Tunali, Small response surface designs for metamodel estimation, European Journal of Operational Research, 145, 455-470, 2003.
Benveniste A., M. Metivier, and P. Priouret, Adaptive Algorithms and Stochastic Approximations, 1990.
Berry V. Michael, and A.N. Wilson, Black-and-white fringes and the colors of caustics, Applied Optics, 33 (21), 4714-4718, 1994.
Berry Michael W., Brett C. Hazen, Rhonda L. MacIntyre, Richard O. Flamm, Lucas: a system for modeling land-use change, IEEE Computational Science & Eengineering, 3 (1), 24-35, 1996.
Beyn W., and W. Kless, Numerical Taylor expansions of invariant manifolds in large dynamical systems, Numerische Mathematik, 80 (1), 1-38, 1998.
Bhatnagar S., and I. Reddy, Optimal threshold policies for admission control in communication networks via discrete parameter stochastic approximation, Telecommunication Systems, 29, 9-31, 2005.
Bielli M., A. Boulmakoul and M. Rida, Object oriented model for container terminal distributed simulation, European Journal of Operational Research, 175, 1731-1751, 2006.
Biethhan J., and V. Nissen, Combinations of simulation and evolutionary algorithms in management science and economics, Annals of Operations Research, 52, 183-208, 1994.
Binder K., and D. Heermann, Monte Carlo Simulation in Statistical Physics: An Introduction, 1998.
Bollapragada R., and U. Rao, Replenishment planning in discrete-time, capacitated, non-stationary, stochastic inventory systems, IIE Transactions, 38, 605-617, 2006.
Born R., Packet-switching network performance under real-time voice-call loading, Modeling & Simulation, 1994.
Boukerche A., An adaptive partitioning algorithm for distributed discrete event simulation systems, Journal of Parallel and Distributed Computing, 62, 1454-1475, 2002.
Boukerche A., and T. Tuck, RF-MVTC: An efficient risk-free multiversion concurrency control algorithm, Concurrency Computation Practice and Experience, 16, 1291-1311, 2004.
Bumble M., and L. Coraor, An architecture for a nondeterministic distributed simulator, Burke Speech Processing for IP Networks: Media Resource Control Protocol, 2007.

Camilleri D., Alternative simulation techniques for distortion of thin plate due to fillet-welded stiffeners, Modelling and Simulation in Materials Science and Engineering, 14 (8), 1307-1327, 2006.
Camilleri D., T.G.F. Gray, Computationally efficient welding distortion simulation techniques, Modelling and Simulation in Materials Science and Engineering, 13 (8), 1365-1382, 2005.
Cancela H. and M. Urquhart, Adapting RVR simulation techniques for residual connectedness network reliability models, IEEE Transactions on Computers, 51, 439-443, 2002.
Cao X, Perturbation analysis of discrete event systems: Concepts, algorithms, and applications, European Journal of Operational Research, 91 (1), 1-13, 1996.
Cao Y., H. Sun, and K. Trivedi, Performance analysis of reservation media-access protocol with access and serving queues under bursty traffic in GPRS/EGPRS, IEEE Transactions on Vehicular Technology, 52, 1627-1641, 2003.
Cavallaro J.R., Architectures for heterogeneous multi-tier networks, Wireless Personal Communications, 22 (2), 285-296, 2002.
Chai C. and S. Sun, Study on the technique of man-machine simulation based on posture prediction, Journal of Computational Information Systems, 2 (2), 897-904, 2006.
Chan A., R.W.H. Lau, B. Ng, Motion prediction for caching and prefetching in mouse-driven DVE navigation, ACM Transactions on Internet Technology, 5 (1), 70-81, 2005.
Chen E., and W. Kelton, Determining simulation run length with the runs test, Simulation Modelling Practice and Theory, 11, 237-250, 2003.
Cheng Y., G. Xu, D. Zhu, W. Zhu, and L. Luo, Thermal analysis for indirect liquid cooled MultiChip module using computational fluid dynamic simulation and response surface methodology, IEEE Transactions on Components and Packaging Technologies, 29, 39-46, 2006.
Chin D., Comparative study of stochastic algorithms for system optimization based on gradient approximation, IEEE Transactions on Systems Man, and Cybernetics - Part B: Cybernetics, 27(2), 244-249, 1997.
Chin K.-S., X. Zu, C.K. Mok, H.Y. Tam, Integrated integration definition language 0 (IDEF) and coloured Petri nets (CPN) modelling and simulation tool: A study on mould-making processes, International Journal of Production Research, 44 (16), 3179-3205, 2006.
Choi D.-H., Cooperative mutation based evolutionary programming for continuous function optimization, Operations Research Letters, 30, 195-201, 2002.
Chow A.H.F., and H.K. Lo, Sensitivity analysis of signal control with physical queuing: Delay derivatives and an application, Transportation Research Part B: Methodological, 41 (4), 462-477, 2007.
Chu L. and B. Wah, Optimal mapping of neural-network learning on message-passing multicomputers, Journal of Parallel and Distributed Computing, 14 (3), 319-339, 1992.
Chung C., Simulation Modeling Handbook: A Practical Approach, 2003.
Chung M., and J. Xu, An overhead reducing technique for time warp, Journal of Parallel and Distributed Computing, 65, 65-73, 2005.
Clark D, Necessary and sufficient conditions for the Robbins-Monro method, Stochastic Processes and Their Applications, 17 (3), 359-367, 1984.
Cloud D., Applied Modeling and Simulation, 1998.
Clymer J., System design and evaluation using discrete event simulation with AI, European Journal of Operational Research, 84, 213-225, 1995.

D'Acquisto G., and M. Naldi, Computational costs of fast stochastic simulation techniques for Markovian fluid models in multiservice networks, Simulation Practice and Theory, 9, 255-272, 2002.
Dai G.-H., and Z.-B. Ren, Study of microsphere extinction efficiency by Matlab language computer simulation, Infrared and Laser Engineering, 33 (3), 231-234, 2004.
Davison A. , and D. Hinkley, Bootstrap Methods and their Application, 1997.
De Lara J., and H. Vangheluwe, Defining visual notations and their manipulation through meta-modelling and graph transformation, Journal of Visual Languages and Computing, 15, 309-330, 2004.
Deelman E., and B. Szymanski, Simulating spatially explicit problems on high performance architectures, Journal of Parallel and Distributed Computing, 62, 446-467, 2002.
Delaney W., and E. Vaccari, Dynamic Models and Discrete Event Simulation, 1989.
Di Martino B., S. Briguglio, G. Vlad, and P. Sguazzero, Parallel PIC plasma simulation through particle decomposition techniques, Parallel Computing, 27, 295-314, 2001.
Díaz-Emparanza I, Is a small Monte Carlo analysis a good analysis? Checking the size power and consistency of a simulation-based test, Statistical Papers, 43, 567-577, 2002.
Di´ez L.I., C. Corte´s, and A. Campo. Modelling of pulverized coal boilers: Review and validation of on-line simulation techniques, Applied Thermal Engineering, 25 (10), 1516-1533, 2005.
Dippon J., and J. Renz, Weighted means in stochastic approximation of minima, SIAM Journal of Control and Optimization, 35 (5), 1811-1827, 1997.
Doung C., Development of a process model for the vacuum assisted resin transfer molding simulation by the response surface method, Composites Part A: Applied Science and Manufacturing, 37, 1316-1324, 2006.
Dong X., G. Meng, and J. G Peng, Vibration control of piezoelectric smart structures based on system identification technique: Numerical simulation and experimental study, Journal of Sound and Vibration, 297, 680-693, 2006.
Dong X., and T.H. Lai, Dynamic Carrier Allocation Strategies for Mobile Cellular Networks, Journal of Parallel and Distributed Computing, 61 (7), 926-949, 2001.
Donohue J., E. Houck, and R. Myers, Simulation design for the estimation of quadratic response surface gradients in the presence of model mis-specification, Management Science, 41 (2), 244-262, 1995.
Dotoli M., and M. Fanti, A coloured Petri net model for automated storage and retrieval systems serviced by rail-guided vehicles: A control perspective, International Journal of Computer Integrated Manufacturing, 18, 122-136, 2005.
Dullaert W., B. Vernimmen, B. Raa, and F. Witlox, A hybrid approach to designing inbound-resupply strategies, IEEE Intelligent Systems, 20, 31-35, 2005.
On statistical sensitivity analysis in stochastic programming, Annals of Operations Research, 30, 199-214, 1991.
Dussault J., D. Labrecque, P. L’Ecuyer, and R. Rubinstein, Combining the stochastic counterpart and stochastic approximation methods, Discrete Event Dynamic Systems: Theory and Applications, 7 (1), 5-28, 1991.

Efron B., and R. Tibshirani, An Introduction to the Bootstrap, 1993.
Ermoliev Y., and V. Norkin, Normalized convergence in stochastic optimization, Annals of Operations Research, 30, 187-198, 1991.

Fallahi, A., and E. Hossain, Distributed and energy-aware MAC for differentiated services wireless packet networks: A general queuing analytical framework, IEEE Transactions on Mobile Computing, 6 (4), 381-394, 2007.
Ferro, A., I. Delgado, A. Mun~oz, and F. Liberal, An analytical model for loss estimation in network traffic analysis systems , Journal of Computer and System Sciences, 72 (7), 1121-1133, 2006.
Filho W., C. Hirata, and E.Yano, GroupSim: A collaborative environment for discrete event simulation software development for the World Wide Web, Simulation, 80, 257-272, 2004.
Fishman G., Monte Carlo, Concepts, Algorithms, and Applications, 1996.
Fishman G., Discrete-Event Simulation: Modeling, Programming and Analysis, 2001.
Fishwick P., Simulation Model Design and Execution: Building Digital Worlds, 1995.
Fletcher R., Practical Methods of Optimization, 1987.
Fonnesbeck C., Spatial modeling of riparian state dynamics in eastern Oregon, USA by using discrete event simulation, Landscape and Urban Planning, 80, 268-277, 2007.
Fortino G., C. Mastroianni, and W. Russo, Cooperative control of multicast-based streaming on-demand systems, Future Generation Computer Systems, 21, 823-839, 2005.
Fournie´ L., D. Hong, and F. Perisse, NetScale: Scalable time-stepped hybrid simulation of large IP networks, Computer Communication Review, 36, 35-38, 2006.
Franke C., B. Basdere, M. Ciupek and S. Seliger, Remanufacturing of mobile phones: Apacity, program and facility adaptation planning, Omega, 34, 562-570, 2006.
Frey P., Radhakrishnan R., Carter H., P. Wilsey, and P. Alexander, A formal specification and verification framework for time warp-based parallel simulation, IEEE Transactions on Software Engineering, 28, 58-78, 2002.
Friedman L., The Simulation Metamodel, 1996.
Fu M., Optimization via simulation: A review, Annals of Operations Research , 53, 199-247, 1994.
Fu M., and J-Q. Hu, Conditional Monte Carlo: Gradient Estimation and Optimization Applications, 1997.
Fujimoto R., Parallel and Distributed Simulation Systems, 2001
Richard M., Fujimoto Network Simulation (Synthesis Lectures on Communication Networks), 2006.
Futschik A., and G. Pflug, Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms, European Journal of Operational Research, 101(1), 245-260, 1997.

Gajvoronskij A., Optimization of stochastic discrete event dynamic systems: A survey of some recent results, Lect. Notes Econ. Math. Syst., 374, 24-44, 1992.
Gala´n-Mari´n G., E. Me´rida-Casermeiro, and J. Mun~o-Pe´rez, Modelling competitive Hopfield networks for the maximum clique problem, Computers and Operations Research, 30, 603-624, 2003.
Gambardella L., A. Rizzoli, and P. Funk, Agent-based planning and simulation of combined rail/road transport, Simulation, 78, 293-303, 2002.
García I., and R. Mollá, Using a discrete event simulator as real time graphic applications kernel, Simulation Modelling Practice and Theory, 14, 1043-1056, 2006.
Garci´a I., and R. Molla´, Videogames decoupled discrete event simulation, Computers and Graphics, 29, 195-202, 2005.
Gauthier P.-A., A. Berry, and W. Woszczyk, Sound-field reproduction in-room using optimal control techniques: Simulations in the frequency domain, Acoustical Society of America Journal, 117 (2), 662-678, 2005.
Ghosh S., Ghosh Innovative Network Analysis and Design: A Modeling and Simulation Approach, 2007.
Ghosh S., and T. Lee, Modeling & Asynchronous Distributed Simulation: Analyzing Complex Systems, IEEE Publications, 2000.
Gigabit Ethernet hosts, Information Sciences, 176, 3735-3756, 2006.
Gilbert N., and K. Troitzsch, Simulation for the Social Scientist, 1999.
Gilliard J., and C. Ritter, Simulations of liquid chromatography-diode array detector data including instrumental artefacts for the evaluation of mixture analysis techniques, Journal of Chromatography, A758, 1-18, 1997.
Gimblett R., Integrating Geographic Information Systems and Agent-Based Modeling: Techniques for Simulating Social and Ecological Processes, 2002.
Glover F., and M. Laguna, Tabu Search, 1997.
Goldberg D., Genetic Algorithms in Search, Optimization and Machine Learning, 1994.
Grieco A., F. Nucci, and A. Anglani, Representation of fuzzy time variables in discrete event simulation, Integrated Computer-Aided Engineering, 10, 305-318, 2003.
Gross D. and Harris C., Fundamentals of Queueing Theory, 1998.
Guariso G. , M. Hitz, and H. Werthner, An integrated simulation and optimization modelling environment for decision support, Decision Support Systems, 16(1), 103-117, 1996.
Gudmundsson D., and K. Goldberg, Optimizing robotic part feeder throughput with queueing theory, Assembly Automation, 27, 134-140, 2007.
Guo S., X. Liao, C. Li, and D. Yang, Stability analysis of a novel exponential-RED model with heterogeneous delays, Computer Communications, 30 (5), 1058-1074, 2007.
Gustafsson L., Poisson simulation as an extension of continuous system simulation for the modeling of queuing systems, Simulation, 79, 528-541, 2003.
Gutie´rrez E., S. Romero, L.F. Romero, O. Plata, E.L. Zapata, Parallel techniques in irregular codes: Cloth simulation as case of study, Journal of Parallel and Distributed Computing, 65 (4), 424-436, 2005.
Gyires T., Simulation of the harmful consequences of self-similar network traffic, Journal of Computer Information Systems, 42, 94-111, 2002.

Haas P., Stochastic Petri Net Models Modeling and Simulation, 2002.
Hamada M., H. Martz, E. Berg, and A. Koehler, Optimizing the product-based availability of a buffered industrial process, Reliability Engineering & System Safety, 91, 1039-1048, 2006.
Hamilton J., Time Series Analysis, 1994. Harrington J., and K. Tumay, Simulation Modeling Methods: An Interactive Guide to Results-Based Decision, 1998.
Hasan S., D. Melo, and Rubens M. Filho, Simulation and response surface analysis for the optimization of a three-phase catalytic slurry reactor, Chemical Engineering and Processing, 44, 335-343, 2005.
Hazra M., D. Morrice, and S. Park, A simulation clock-based solution to the frequency domain experiment indexing problem, IIE Transactions, 29 (9) 769-782, 1997.
Heath D. and P. Sanchez, On the adequacy of pseudo-random number generators: How big a period do we need?, Operations Research Letters, 5, 3-6, 1986.
Hemkumar Nariankadu D., and Joseph R. Cavallaro, Simulation of systolic arrays on the connection machine, Simulation, 61 (3), 151-160, 1993.
Hidaka S., T. Aoki, H. Aida, and T. Saito, Implementation and performance evaluation of a FIFO Queue class library for time warp, Systems and Computers in Japan, 33, 90-98, 2002.
Hill D., Object-Oriented Analysis and Simulation Modeling, 1996.
Hines M., and N. Carnevale, Discrete event simulation in the NEURON environment, Neurocomputing, 58-60:1117-1122, 2004.
Ho Y. , S. Leyuan, D. Liyi, and W. Gong, Optimizing discrete event dynamic systems via the gradient surface method, Discrete Event Dynamic Systems: Theory and Applications, 2(1), 99-120, 1992.
Hollmann, J., A. Ardö., and P. Stenström, Effectiveness of caching in a distributed digital library system Journal of Systems Architecture 53 (7), 403-416, 2007.
Hong K.J., and T.G. Kim, DEVSpecL: DEVS specification language for modeling, simulation and analysis of discrete event systems, Information and Software Technology, 48 (4), 221-234, 2006.
Hooke R., and T. Jeeves, A direct search solution of numerical and statistical problems, Journal of Association for Computing Machinery, 8 (2), 212-229, 1961.
Hossain M., M. Hassan, and H. Sirisena, Adaptive resource management in mobile wireless networks using feedback control theory, Telecommunication Systems, 25, 401-415, 2004.
Hounkpevi F.O., and E.E. Yaz, Minimum variance generalized state estimators for multiple sensors with different delay rates, Signal Processing, 87 (4), 602-613, 2007.
Hsu M., Lin Y., B. Li, and M. Chang, A SIP-based call center with waiting time prediction, Journal of Internet Technology, 7, 313-322, 2006.
Huang D., R. Scholz, W. Gujer, D. Chitwood, P. Loukopoulos, R. Schertenleib, and H. Siegrist, Discrete event simulation for exploring strategies: An urban water management case, Environmental Science and Technology, 41, 915-921, 2007.
Hwang W.-S., J.-H. Ho, and C.-K. Shieh, Modeling IP packets over WDM ring network with one tunable transmitter and multiple fixed receivers (TT-FRs), Journal of Information Science and Engineering, 23 (1), 259-270, 2007.
Hybinette M., and R. Fujimoto, Latency hiding with optimistic computations, Computer Networks and ISDN Systems, 26, 1447-1456, 1994.

Ibidapo-Obe O., O. Asaolu, and A. Badiru, A New Method for the Numerical Solution of Simultaneous Nonlinear Equations, Applied Mathematics and Computation, 125, 133-140, 2002.
Islam T., C. Pramanik, and H. Saha, Modeling, simulation and temperature compensation of porous polysilicon capacitive humidity sensor using ANN technique, Microelectronics Reliability, 45, 697-703, 2005.

Jacobson S., and L. Schruben, Techniques for simulation response optimization, Operations Research Letters, 8 (1), 1989.
Janke W., Introduction to simulation techniques, Lecture Notes in Physics, 207-260, 2007.
Javadi, B., M.K. Akbari, and J.H. Abawajy, Analytical communication networks model for enterprise Grid computing , Future Generation Computer Systems, 23 (6), 737-747, 2007.
Jayalath A., and C. Tellambura, Use of data permutation to reduce the peak-to-average power ratio of an OFDM signal, Wireless Communications and Mobile Computing, 2, 187-203, 2002.
Jelger C., and J. Elmirghani, Performance evaluation of a new MAC protocol for WDM metropolitan access ring networks, International Journal of Communication Systems, 15, 191-202, 2002.
Jia Q, Y. Ho, and Zhao Q. Y.-C., Comparison of selection rules for ordinal optimization, Mathematical and Computer Modelling, 43, 1150-1171, 2006.
Jiang J., T.-H. Lai, and N. Soundarajan, On distributed dynamic channel allocation in mobile cellular networks, IEEE Transactions on Parallel and Distributed Systems, 13 (10), 1024-1037, 2002.
Jiang J.-R., Y.-C. Tseng, C.-S. Hsu, and T.-H Lai, Quorum-based asynchronous power-saving protocols for IEEE 802.11 ad hoc networks, Mobile Networks and Applications, 10 (1), 169-181, 2005.
Jime´nez A., A. Mateos, and S. Ri´os-Insua, Monte Carlo simulation techniques in a decision support system for group decision making, Group Decision and Negotiation, 14 (2), 2005.
Jones B.A., and J.R. Cavallaro, A rapid prototyping environment for wireless communication embedded systems, EURASIP Journal on Applied Signal Processing, (6), 603-614, 2003.

Kangsabanik P., D.S. Yadav, R. Mall, and A.K. Majumdar, Performance analysis of long-lived cooperative transactions in active, DBMS Data and Knowledge Engineering, 62 (3), 547-577, 2007.
Karaboga D., and D. Pham, Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks, 1998.
Kari H., J. Salinas, and F. Lombarda, Generating non-standard random distributions for discrete event simulation systems, Simulation Practice and Theory, 1, 173-193, 1994.
Karlsson G., V. Lenders, and M. May, Delay-tolerant broadcasting, IEEE Transactions on Broadcasting, 53 (1), 369-381, 2007.
Katsaros P., and C. Lazos, Technique for determining queuing network simulation length based on desired accuracy, Computer Systems Science and Engineering, 15, 399-404, 2000.
Kattan M., and R. Cooper, A simulation of factors affecting machine learning techniques: An examination of partitioning and class proportions, Omega, 28, 501-512, 2000.
Katz S., S. Zlochiver, and S. Abboud, Induced current Bio-impedance technique for monitoring bone mineral density-a simulation model , Annals of Biomedical Engineering 34 (8), 1332-1342, 2006.
Kavic?ka A., V. Klima, and N. Adamko, Analysis and optimisation of railway nodes using simulation techniques, WIT Transactions on the Built Environment 88, 663-672, 2006.
Kesgin U., and H. Heperkan, Simulation of thermodynamic systems using soft computing techniques, International Journal of Energy Research, 29 (7), 581-611, 2005.
Kiefer J., and J. Wolfowitz, Stochastic estimation of the maximum of a regression function, Annals of Mathematical Statistics, 23 (5), 462-466, 1952.
Kiehl T., R. Mattheyses, and M. Simmons, Hybrid simulation of cellular behavior, Bioinformatics, 20, 316-322, 2004.
Kilgore R.A., Object-oriented simulation with Java, Silk, and OpenSML .Net languages, Winter Simulation Conference Proceedings, 1, 227-233, 2002.
Kim K., Implementation of stabilizing receding horizon controls for time-varying systems, Automatica, 38, 1705-1711, 2002.
Kim T., J. Lee, and P. Fishwick, A two-stage modeling and simulation process for web-based modeling and simulation, ACM Transactions on Modeling and Computer Simulation,12, 230-248, 2002.
Kim H.S., M.S. Shin, D.S. Jang, S.H. Jung, and J.H. Jin, Study of flow characteristics in a secondary clarifier by numerical simulation and radioisotope tracer technique, Applied Radiation and Isotopes, 63 (4), 519-526, 2005.
Kleijnen J., and W. Van Groenendaal, Simulation: A Statistical Perspective, 1992.
Kleijnen J., Regression metamodel for generalizing simulation results, IEEE Transactions on Systems, Man and Cybernetics, 93-96, 1979.
Koehler G., New directions in genetic algorithm theory, Annals of Operations Research, 75, 49-68, 1997.
Kofman E., Second-order approximation for DEVS simulation of continuous systems, Simulation, 78, 76-89, 2002.
Koriem S.M., Development, analysis and evaluation of performance models for mobile multi-agent networks, Computer Journal, 49 (6), 685-709, 2006.
Korn G., Model replication techniques for parameter-influence studies and Monte Carlo simulation with random parameters, Mathematics and Computers in Simulation, 67, 501-513, 2005.
Kouikoglou V., and Y. Phillis, Hybrid Simulation Models of Production Networks, 2001.
Kouskouras K., and A. Georgiou, A discrete event simulation model in the case of managing a software project, European Journal of Operational Research, 181, 374-389, 2007.
Kubota H., Y. Tanji, T. Watanabe, and H. Asai, An enhanced time-domain circuit simulation technique based on LIM, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E89-A (5), 1505-1506, 2006.
Kuhl F., R. Weatherly, and J. Dahmann, Creating Computer Simulation Systems: An Introduction to the High Level Architecture, 1999.
Kurose J., and K. Ross, Computer Networking: A Top-Down Approach Featuring the Internet, 2004.
Kuusilinna K., J. Riihima¨ki, T. Ha¨ma¨la¨inen, and J. Saarinen, DTNS: A discrete time network simulator for C/C++ language based digital hardware simulations, Advances in Physics, Electronics and Signal Processing Applications, 123-128, 2000.

Lackovic M., and C. Bungarzeanu, Modeling and performance analysis of IP access interface in optical transmission networks with packet switching, Telecommunication Systems, 32, 131-148, 2006.
Lamb J., and R. Cheng, Optimal allocation of runs in a simulation metamodel with several independent variables, Operations Research Letters, 30, 189-194, 2002.
Law A., and W. Kelton, Simulation Modeling and Analysis, 1999.
Lazzez A., Y. Khlifi, S. Guemara El Fatmi, N. Boudriga, and M.S. Obaidat, A novel node architecture for optical networks: Modeling, analysis and performance evaluation, Advances in Computer Communications Networks, 30 (5), 923-1164, 2007.
L'Ecuyer P., and G. Yin, Rates of convergence for budget dependent stochastic optimization algorithms, Proceedings of the 35th IEEE Conference on Decision and Control, 1069-1070, 1996.
Lee C., H. Huang, B. Liu, and Z. Xu, Development of timed Colour Petri net simulation models for air cargo terminal operations, Computers & Industrial Engineering, 51, 102-110, 2006.
Lee J., and B. Zeigler, Space-based communication data management in scalable distributed simulation, Journal of Parallel and Distributed Computing, 62, 336-365, 2002.
Lee J., B. Kim, and J. Shin, Performance analysis of IPACT media access control protocols for Gigabit Ethernet-PONs, IEICE Transactions on Communications, E90-B (4), 845-855, 2007.
Lee M., and S. Chen, A software engineering approach to develop a discrete event simulation model, WSEAS Transactions on Computers, 4, 1240-1248, 2005.
Lee T., and G. Sandison, The energy-dependent electron loss model: Backscattering and application to heterogeneous slab media, Physics in Medicine and Biology, 48, 259-273, 2003.
Lee Y., A virtual server queueing network method for component based performance modelling of metacomputing, Future Generation Computer Systems, 20 (1), 145-155, 2004.
Lee, J., and T. Kim, Active multicast congestion control with hop-by-hop credit-based mechanism, IEICE Transactions on Communications, E85-B (3), 614-622, 2002.
Lee K.-I., C. Lee, H. Shin, Y.J. Park, and H.S. Min, Efficient frequency-domain simulation technique for short-channel MOSFET, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 24 (6), 862-867, 2005.
Leemis L., and S. Park, Discrete-Event Simulation: A First Course, 2005.
Lei H., and A.A. Nilsson, Queuing analysis of power management in the IEEE 802.11 based wireless LANs, IEEE Transactions on Wireless Communications, 6 (4), 1286-1294, 2007.
Lepadatu D., R. Hambli, A. Kobi, and A. Barreau, Optimisation of springback in bending processes using FEM simulation and response surface method, International Journal of Advanced Manufacturing Technology, 27, 40-47, 2005.
Leu Dar-Ren, Farokh B. Bastani, and Ernst L. Leiss, Effect of statically & dynamically replicated components on system reliability, IEEE Transactions on Reliability, 39 (2), 209-216, 1990.
Leuschen M.L., I.D. Walker, and J.R. Cavallaro, Evaluating the reliability of prototype degradable systems, Reliability Engineering and System Safety, 72 (1), 9-20, 2001. Li G., and B. Wah, Computational efficiency of parallel combinatorial OR-tree searches, IEEE Transactions on Software Engineering, 16, 13-31, 1990.
Li W., and C. Cassandras, A cooperative receding horizon controller for multivehicle uncertain environments, IEEE Transactions on Automatic Control, 51, 242-257, 2006.
Li W., and C. Cassandras, Centralized and distributed cooperative receding horizon control of autonomous vehicle missions, Mathematical and Computer Modelling, 43, 1208-1228, 2006.
Li D.H.W., S.L. Wong, C.L. Tsang, and G.H.W. Cheung, A study of the daylighting performance and energy use in heavily obstructed residential buildings via computer simulation techniques, Energy and Buildings, 38 (11), 1343-1348, 2006.
Li L.W.F., F.W.B. Li, and R.W.H. Lau, A trajectory-preserving synchronization Method for collaborative visualization, IEEE Transactions on Visualization and Computer Graphics, 12 (5), 989-996, 2006.
Li Y., A two-dimensional thin-film transistor simulation using adaptive computing technique, Applied Mathematics and Computation, 73-85, 2007.
Li Y., and C.-K. Chen, A simulation-based evolutionary technique for inverse doping profile problem of sub-65 nm CMOS devices, Journal of Computational Electronics, 5 (4), 365-370, 2006.
Lian Z., B. Jiao, and X. Gu, A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan, Applied Mathematics and Computation, 183, 1008-1017, 2006.
Liljenstam M., Ro¨nngren R., and R. Ayani, MobSim++: Parallel simulation of personal communication networks, IEEE Distributed Systems, 2, 111-119, 2001.
Lin .-W., Providing fault-tolerant authentication and authorization in wireless mobile IP networks, Journal of Systems and Software, 80 (2), 149-163, 2007.
Lindemann C., and A. Thümmler, Performance analysis of the general packet radio service, Computer Networks, 41, 1-17, 2003.
Ljungberg M., A. Larsson, L. Johansson, A new collimator simulation in SIMIND based on the delta-scattering technique, IEEE Transactions on Nuclear Science, 52 (5), 1370-1375, 2005.
Louvros S., J. Pylarinos, and S. Kotsopoulos, Mean waiting time analysis in finite storage queues for wireless cellular networks, Wireless Personal Communications, 40 (2), 145-155, 2007.
Loyka S., The influence of electromagnetic environment on operation of active array antennas: Analysis and simulation techniques, IEEE Antennas and Propagation Magazine, 41, 23-39, 1999.
Loyka S., and J. Mosig, New behavioral-level simulation technique for RF/microwave applications. Part I: Basic concepts, International Journal of RF and Microwave Computer-Aided Engineering, 10, 221-237, 2000.
Lu M., and L. Wong, Comparison of two simulation methodologies in modeling construction systems: Manufacturing-oriented PROMODEL vs. construction-oriented SDESA, Automation in Construction, 16, 86-95, 2007.

Ma M., and Q. Zhu, Providing real-time service in CDMA wireless networks, Wireless Personal Communications, 41 (4), 551-562, 2007.
MacKeown P., Stochastic Simulation in Physics, Springer, New York, 1997.
Maksimey I.V., and V.S. Smorodin, Technique of simulation modeling of control systems of dangerous manufacture, Journal of Automation and Information Sciences, 37 (7), 46-53, 2005.
Michel Mandjes, Large Deviations for Gaussian Queues: Modelling Communication Networks, 2007.
Mateos A., A. Jime´nez, and S. Ri´os-Insua, Monte Carlo simulation techniques for group decision making with incomplete information, European Journal of Operational Research, 174 (3), 1842-1864, 2006.
McGill K.C., Optimal resolution of superimposed action potentials, IEEE Transactions on Biomedical Engineering, 49, 640-650, 2002.
McNally P., and C. Heavey, Developing simulation as a desktop resource, International Journal of Computer Integrated Manufacturing, 17, 435-450, 2004.
Medaglia A., Fang S., and H. Nuttle, Fuzzy controlled simulation optimization, Fussy Sets and Systems, 127 (1), 65-84, 2002.
Merkuryev Y., and V. Visipkov, A survey of optimization methods in discrete systems simulation, First Joint Conference of International Societies Proceedings, 104-110, 1996.
Mertins K., M. Rabe, and F. Ja¨kel, Distributed modelling and simulation of supply chains, International Journal of Computer Integrated Manufacturing, 18, 342-344, 2005.
Mine T., H. Kubota, A. Kamo, T. Watanabe, and H. Asai, An efficient simulation method of linear/nonlinear mixed circuits based on hybrid model order reduction technique, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E87-A (9), 2274-2279, 2004.
Mis?ic´ J., V. Mis?ic´, and K. Chan, Performance of Bluetooth bridge scheduling algorithms, Computer Communications, 27, 1143-1151, 2004.
Mizrak P., and G. Bayhan, Comparative study of dispatching rules in a real-life job shop environment, Applied Artificial Intelligence, 20, 585-607, 2006.
Mohamed M.E., H.K.M. Youssef, and M.M. Abdel-Aziz, Genetic based thermal charge simulation technique, Journal of Engineering and Applied Science, 52 (5), 1019-1034, 2005.
Moon Y., and D. Phatak, Enhancing ERP system's functionality with discrete event simulation, Industrial Management and Data Systems, 105, 1206-1224, 2005.
Moreno-Castaneda V., C. Pislaru, J. Freeman, and D. Ford, Modelling and simulation of a feed drive using the transmission line modeling technique, Laser Metrology and Machine Performance VI, 2003.
Morrice D., and Bardhan I., A weighted least squares approach to computer simulation factor screening, Operations Research, 43, 792-806, 1995.
Morrison A., S. Straube, H.E. Plesser, M. Diesmann, Exact subthreshold integration with continuous spike times in discrete-time neural network simulations, Neural Computation, 19 (1), 47-79, 2007.
Mu¨ller M., and J.J. de Pablo, Simulation techniques for calculating free energies, Lecture Notes in Physics, 703, 67-126, 2006.
Murayama M., F. Togashi, K. Nakahashi, K. Matsushima, and T. Kato, Simulation of aircraft response to control surface deflection using unstructured dynamic grids, Journal of Aircraft, 42, 340-346, 2005.

Naldi M., and F. Calonico, A comparison of the GEVT and RESTART techniques for the simulation of rare event s in ATM networks, Simulation Practice and Theory, 6, 181-196, 1998.
Nasereddin M., M. Mullens, and D. Cope, Automated simulator development: A strategy for modeling modular housing production, Automation in Construction, 16, 212-223, 2007.
Nelson B., Stochastic Modeling: Analysis & Simulation, 1995.
Neurocomputing, 58-60, 1117-1122, 2004.
Ng B., R.W.H. Lau, A. Si, and F.W.B. Li, Multiserver support for large-scale distributed virtual environments, IEEE Transactions on Multimedia, 7 (6), 1054-1064, 2005.
Nicola V., P. Shahabuddin, and M. P., Nakayama, Techniques for fast simulation of models of highly dependable systems, IEEE Transactions on Reliability, 50, 246-264, 2001.
Nicola V.F., and T.S. Zaburnenko, Efficient importance sampling heuristics for the simulation of population overflow in Jackson networks, ACM Transactions on Modeling and Computer Simulation, 17 (2), 2007.
Nikoukaran J., Software selection for simulation in manufacturing: A review, Simulation Practice and Theory, 7, 1-14, 1999.
Niyato D., E. Hossain, and A. Fallahi, Sleep and wakeup strategies in solar-powered wireless sensor/mesh networks: Performance analysis and optimization, IEEE Transactions on Mobile Computing, 6 (2), 221-236, 2007.
Noguera J., and E. Watson, Response surface analysis of a multi-product batch processing facility using a simulation metamodel, International Journal of Production Economics, 102, 333-343, 2006.

Oakshott L., Business Modelling and Simulation, 1997.
Obaidat M., and B. Sadoun, A Simulation Evaluation study of neural network techniques to computer user identification, Information Sciences, 102, 239-258, 1997.
Okada M., S. Hara, S. Komaki, and N. Morinaga, An application of simulated annealing to the design of block coded modulation, IEICE Transactions on Communications, E79-b(1), 88-91, 1996.
O'Kane J., Simulating production performance: Cross case analysis and policy implications, Industrial Management and Data Systems, 104, 309-321, 2004.
Oliveira-Esquerre K., A. Da Costa, R. Bruns, and M. Mori, Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques, Applied Biochemistry and Biotechnology - Part A Enzyme Engineering and Biotechnology, 106, 437-450, 2003.
Oraizi H., M. Moradian, and K. Hirasawa, Design and optimization of microstrip parallel-coupled-line bandpass filters incorporating impedance matching, IEICE Transactions on Communications E89-B (11), 2982-2988, 2006.
Orsoni A., Fuzzy and simulation-based techniques for industrial safety and risk assessment, International Journal of General Systems 35 (5), 619-635, 2006.

Paris S., S. Donikian, and N. Bonvalet, Environmental abstraction and path planning techniques for realistic crowd simulation, Computer Animation and Virtual Worlds, 17 (3-4), 325-335, 2006.
Paris S., S. Donikian, and N. Bonvalet, Environmental abstraction and path planning techniques for realistic crowd simulation, Computer Animation and Virtual Worlds, 17 (3-4), 325-335, 2006.
Park M. , and Y. Kim, A systematic procedure for setting parameters in simulated annealing algorithms, Computers and Operation Research, 25(3), 207-217, 1998.
Park E.-C., P.-Y. Kim, C.-H. Choi, and J. So, Improving quality of service and assuring fairness in WLAN access networks, IEEE Transactions on Mobile Computing, 6 (4), 337-350, 2007.
Patterson D., and J. Hennessy, Computer Organization and Design: The Hardware/Software Interface, The Morgan Kaufmann Series in Computer Architecture and Design, 2004.
Paul J., D. Thomas, and A. Cassidy, High-level modeling and simulation of single-chip programmable heterogeneous multiprocessors, ACM Transactions on Design Automation of Electronic Systems, 10, 431-461, 2005.
Payne M.C., G. Csa´nyi, T. Albaret, and A. De Vita, A novel quantum/classical hybrid simulation technique, ChemPhysChem, 6 (9), 1731-1734, 2005.
Pees S., A. Hoffmann, and H. Meyr, Retargetable compiled simulation of embedded processors using a machine description language, ACM Transactions on Design Automation of Electronic Systems, 5 (4), 815-834, 2000.
Penker A., M. Barbu, and M. Gronalt, Bottleneck analysis in MDF-production by means of discrete event simulation, International Journal of Simulation Modelling, 6, 49-57, 2007.
Peralta J., P. Anussornnitisarn, and S. Nof, Analysis of a time-out protocol and its applications in a single server environment, International Journal of Computer Integrated Manufacturing, 16, 1-13, 2003.
Pflug G., Optimization of Stochastic Models: The Interface Between Simulation and Optimization, 1996.
Pichevar R., J. Rouat and L. Tai, The oscillatory dynamic link matcher for spiking-neuron-based pattern recognition, Neurocomputing, 69, 1837-1849, 2006.
Pidd M., Computer Simulation in Management Science, 1998.
Pierreval H., and Tautou, Using evolutionary algorithm and simulation for the optimization of manufacturing systems, IIE Transactions, 29 (1), 181-189, 1997.
Polak E., Optimization: Algorithms and Consistent Approximations, 1997.
Poo G., and H. Wang, Multi-path routing versus tree routing for VPN bandwidth provisioning in the hose model, Computer Networks, 51, 1725-1743, 2007.
Pooch U., and J. Wall, Discrete Event Simulation: A Practical Approach, 1993.
Potrc I., T. Lerher, J. Kramberger and M. Šraml, Simulation model of multi-shuttle automated storage and retrieval systems, Journal of Materials Processing Technology, 157-158, 236-244, 2004.
Potter A., B. Yang, and C. Lalwani, A simulation study of despatch bay performance in the steel processing industry, European Journal of Operational Research, 179 (2), 567-578, 2007.
Pym,D., and C. Tofts, Systems Modelling via resources and processes: Philosophy, calculus, semantics, and logic, Electronic Notes in Theoretical Computer Science, 172, 545-587, 2007.

Qadan Osama, and Mohsen Guizani, Network Modelling and Simulation: Concepts and Applications, 2007.
Qi D., and T. Hesketh, An analysis of upscaling techniques for reservoir simulation, Petroleum Science and Technology, 23 (7-8), 827-842, 2005.
Qiao H., X. Wang., G. Li, and K. Huang, Template-based persistence framework for parallel discrete event simulations, Journal of System Simulation, 19, 563-566+574, 2007.
Quaglia F., A restriction of the elastic time algorithm, Information Processing Letters, 83, 243-249, 2002.

Rabelo L., H. Eskandari, T. Shaalan and M. Helal, Value chain analysis using hybrid simulation and AHP, International Journal of Production Economics, 105, 536-547, 2007.
Rahbar A.G.P., and O. Yang, OCGRR: A new scheduling algorithm for differentiated services networks, IEEE Transactions on Parallel and Distributed Systems, 18 (5), 697-710, 2007.
Rajagopal S., S. Bhashyam, J.R. Cavallaro, and B. Aazhang, Efficient VLSI architectures for multiuser channel estimation in wireless base-station receivers, Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 31 (2), 143-156, 2002.
Rajagopal S., S. Rixner, and J.R. Cavallaro, A programmable baseband processor design for software defined radios, Circuits and Systems, 3, 413-416, 2002.
Rajan S., P. Kannan, K. Muneeswaran, and T. Revathi, Modelling and simulation of universal digital AC bridge using ANN technique, International Journal of Modelling and Simulation, 26, 137-141, 2006.
Ramos F., and F. Velasco, A simulation analysis of multiagent-based dynamic request placement techniques, Lecture Notes in Computer Science, 3473, 15-27, 2006.
Rao Ming, Tsung-Shann Jiang, and Jeffrey J.-P. Tsai, Integrated intelligent simulation environment, Simulation, 54 (6), 291-295, 1990.
Rardin R., Optimization in Operations Research, 1998.
Reeves C., and J. Rowe, Genetic Algorithms: Principles and Perspectives, 2002.
Ren B., C. Wang, Parallel impact/penetration finite element method simulation techniques, Explosion and Shock Waves, 25 (3), 260-264, 2005.
Rice S.V., H.M. Markowitz, A. Marjanski, and S.M. Bailey, The SIMSCRIPT III programming language for modular object-oriented simulation, Proceedings - Winter Simulation Conference, 621-630, 2005.
Robbins H., and S. Monro, A stochastic approximation method, Annals of Mathematical Statistics, 22 (5), 400-407, 1951.
Robinson S., A statistical process control approach to selecting a warm-up period for a discrete-event simulation, European Journal of Operational Research, 332-346, 2007.
Robinson S., Analysis of sample-path optimization, Mathematics of Operations Research, 21 (3), 513-528, 1996.
Roccetti M., A. Aldini, M. Bernardo, and R. Gorrieri, QoS evaluation of IP telephony services: A specification language based simulation software tool, Systems Analysis Modelling Simulation, 43 (12), 1747-1759, 2003.
Rojas E., and A. Mukherjee, Interval temporal logic in general-purpose situational simulations, Journal of Computing in Civil Engineering, 19, 83-93, 2005.
Rollans S. and D. McLeish, Estimating the optimum of a stochastic system using simulation, Journal of Statistical Computation and Simulation, 72, 357- 377, 2002.
Romeijn H., Global Optimization by Random Walk Sampling Methods, 1992.
Rosberg Z., A. Zalesky, and M. Zukerman, Packet delay in optical circuit-switched networks, IEEE/ACM Transactions on Networking, 14, 341-354, 2006.
Rotab Khan M.R., Performance comparison of spreadsheet simulation and simulation languages: A case example to minimize textile production cost, International Journal of Computer Applications in Technology, 12 (2), 181-189, 1999.
Rubinstein R, and Melamed B., Modern Simulation and Modeling, 1998.
Rubinstein R., and A. Shapiro, Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method, 1993.
Rudd R.E., D.R. Mason, and A.P. Sutton, Lanczos and recursion techniques for multiscale kinetic Monte Carlo simulations, Progress in Materials Science 52 (2-3), 319-332, 2007.
Rudnev V., Subject-oriented assessment of numerical simulation techniques for induction heating applications, International Journal of Materials and Product Technology, 29 (1-4), 43-51, 2007.

Sadiku Matthew N.O., and Mohammad Ilyas, Simulation of Local Area Networks , 1994.
Sadoun B., Optimizing the operation of a toll plaza system using simulation: A methodology, Simulation, 81, 657-664, 2005.
Safizadeh M., and R. Singnorile, Optimization of simulation via quasi-Newton method, ORSA Journal of Computing, 6 (4), 398-408, 1994.
Safizadeh M., Optimization in simulation: Current issues and the future outlook, Naval Research Logistics, 37(5), 807-825, 1990.
Salah K., On the accuracy of two analytical models for evaluating the performance of Gigabit Ethernet hosts, Information Sciences, 176 (24), 3735-3756, 2006.
Salzmann M., and F. Breitenecker, Genetic algorithms in discrete event simulation, Proceeding of the EUROSIM Conference, 213-218, 1995.
Sanchez S., Sanchez P., Ramberg J., and F. Moeeni, Effective engineering design through simulation, Transactions in Operational Research, 3 (2), 169-185, 1997.
Santoro A., and F. Quaglia, Multiprogrammed non-blocking checkpoints in support of optimistic simulation on myrinet clusters, Journal of Systems Architecture, 53, 659-676, 2007.
Sarangapani J., Wireless Ad hoc and Sensor Networks: Protocols, Performance, and Control (Control Engineering), 2007.
Saviotti P., Applied Evolutionary Economics: New Empirical Methods and Simulation Techniques, 2002.
Schormans J., E. Liu, L. Cuthbert, and J. L. Pitts, A Hybrid technique for accelerated simulation of ATM networks and network elements, ACM Transactions on Modeling and Computer Simulation, 11, 182-205, 2001.
Schroer B., and F. Tseng, Modelling complex manufacturing systems using discrete event simulation, Computers & Industrial Engineering, 14, 455-464, 1988.
Schulze C., and D. Stauffer, Recent developments in computer simulations of language compettion, Computing in Science and Engineering, 8 (3), 60-67, 2006.
Schulze C., and D. Stauffer, Monte Carlo simulation of the rise and the fall of languages, International Journal of Modern Physics, C16 (5), 781-787, 2005.
Schuster P., Prediction of RNA secondary structures: From theory to models and real molecules, Reports on Progress in Physics, 69, 1419-1477, 2006.
Sengupta C., J.R. Cavallaro, W.L. Wilson Jr., and F.K. Tittel, Automated evaluation of critical features in VLSI layouts based on photolithographic simulations, IEEE Transactions on Semiconductor Manufacturing, 10 (4), 482-494, 1997.
Severance F., System Modeling and Simulation: An Introduction, 2001.
Shang J., S. Li, and P. Tadikamalla, Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization, International Journal of Production Research, 42, 3823-3849, 2004.
Shang Y., and B. Wah, A discrete lagrangian-based global-search method for solving satisfiability problems, Journal of Global Optimization, 12, 61-99, 1998.
Shang Y., L. Li, and B. Wah, Optimization design of biorthogonal filter banks for image compression, Information Sciences, 132, 23-51, 2001.
Shapiro A., Simulation-based optimization: Convergence analysis and statistical inference, Communications in Statistics: Stochastic Models, 12 (3), 425-432, 1996.
Sheluhin O., S. Smolskiy, and A. Osin, Self-Similar Processes in Telecommunications, 2007.
Sherman M., and D. Goldsman, Large-sample normality of the batch-means variance estimator, Operations Research Letters, 30, 319-326, 2002.
Sichman J., R. Conte, and N. Gilbert, Multi-Agent Systems and Agent-Based Simulation, 1998.
Sienz J., S.J. Bates, and J.F.T. Pittman, Flow restrictor design for extrusion slit dies for a range of materials: Simulation and comparison of optimization techniques, Finite Elements in Analysis and Design, 42 (5), 430-453, 2006.
Silberschatz A., P. Galvin, and G. Gagne, Operating System Concepts, 2004.
Simon R., and P. Rabinovich, Rapid simulator development for multicast protocol analysis, Software - Practice and Experience, 32, 1-23, 2002.
Simonoff J., Smoothing Methods in Statistics, 1996.
Simpson T., J. Poplinski, P. Koch, and J. Allen, Metamodels for Computer-based Engineering Design: Survey and Recommendations, Engineering with Computers, 17, 129-150, 2001.
Singhal S., and M. Zyda, Networked Virtual Environments: Design and Implementation, 1999.
Sinke J., Development of fibre metal laminates: Concurrent multi-scale modeling and testing, Journal of Materials Science, 41, 6777-6788, 2006.
Smith P., M. Shafi, and H. M. Gao, Quick simulation: A review of importance sampling techniques in communications systems, IEEE Journal on Selected Areas in Communications, 15, 597-613, 1997.
Smorodin V.S., Technique of control and decision making in simulation modeling of technological processes of dangerous manufacture, Journal of Automation and Information Sciences, 2006.
Son I.-H., and Y.-T. Im, Localized remeshing techniques for three-dimensional metal forming simulations with linear tetrahedral elements, International Journal for Numerical Methods in Engineering, 67 (5), 672-696, 2006.
Song D., Raw material release time control for complex make-to-order products with stochastic processing times, International Journal of Production Economics, 103, 371-385, 2006.
Spall J., A one-measurement form of simultaneous perturbation stochastic approximation, Automatica, 33 (1), 109-112, 1997.
Srichander R., Efficient schedules for simulated annealing, Engineering Optimization, 24 (1), 161-176, 1995.
Stuckman B., and P. Stuckman, Design optimization using simulation and stochastic global search: A computer-aided engineering approach, Advances in Modelling and Simulation, 18 (1), 13-33, 1990.
Sun J., M. Smith, L. Smith, and L.-P. Nolte, Simulation of an optical-sensing technique for tracking surgical tools employed in computer-assisted interventions, IEEE Sensors Journal, 5 (5), 1127-1130, 2005.
Sun M.-T., L. Huang, S. Wang, A. Arora, and T.-H. Lai, Reliable MAC layer multicast in IEEE 802.11 wireless networks, Wireless Communications and Mobile Computing, 3 (4), 439-453, 2003.
Sun M.-T., S. Wang, C.-K. Chang, T.-H. Lai, H. Sawatari, and H. Okada, Interference-mitigating MAC scheduling and SAR policies for Bluetooth scatternets, Journal of Circuits, Systems and Computers, 13 (2), 387-398, 2004.
Suri R., and M. Leung, Single run optimization of discrete event simulation-An empirical study using the M/M/1 queue, IIE Transactions, 21(1), 35-49, 1989.
Swidan A., S. El-Ghanam, H. Ashry, F. Soliman, and W. Abdel-Basit, Correlation between simulation, theoretical analysis, and experimental techniques of Butterworth and Chebyshev second order active filters characteristics, Modelling, Measurement and Control, A77, 17-32, 2004.
Szczerbicka H., and P. Ziegler, Simulation with active objects: an approach to combined modeling, Simulation Practice and Theory, 1, 267-281, 1994.

Tang W., R. Goh, and I. Thng, Ladder queue: An O(1) priority queue structure for large-scale discrete event simulation, ACM Transactions on Modeling and Computer Simulation, 15, 175-204, 2005.
Tarasenko G., Stochastic Optimization in the Soviet Union, 1986.
Tatsiopoulos I., N. Panayiotou, and S. Ponis, A modelling and evaluation methodology for E-Commerce enabled BPR, Computers in Industry, 49, 107-121, 2002.
Teymori, S., and W. Zhuang, Queue analysis and multiplexing of heavy-tailed traffic in wireless packet data networks, Mobile Networks and Applications 12 (1), 31-41, 2007.
Tirado J.M., J. Sanchez-Rojas, and J. Izpura, Simulation of surface state effects in the transient response of AlGaN/GaN HEMT and GaN MESFET devices, Semiconductor Science and Technology, 21, 1150-1159, 2006.
Tompkins G., and F. Azadivar, Genetic algorithms in optimizing simulated systems, Proceedings of the Winter Simulation Conference, 757-762, 1995.
Tsai C-Sh., Evaluation and optimisation of integrated manufacturing system operations using Taguch's experiment design in computer simulation, Computers and Industrial Engineering, 43, 591-604, 2002.
Tsai Jeffrey J.P., Steve Jennhwa Yang, and Yao-Hsiung Chang, Timing constraint Petri nets and their application to schedulability analysis of real-time system specifications, IEEE Transactions on Software Engineering, 21 (1), 32-49, 1995.
Tyagi M., S. Roy, A.D. Harvey III, and S. Acharya, Simulation of laminar and turbulent impeller stirred tanks using immersed boundary method and large eddy simulation technique in multi-block curvilinear geometries, Chemical Engineering Science 62 (5), 1351-1363, 2007.
Tyrtyshnikov E., A Brief Introduction to Numerical Analysis, 1997.

Usha M., and R.S.D. Wahida Banu, A proactive mechanism for quality of service control in high speed networks, International Journal of Business Information Systems, 2 (3), 312-327, 2007.
Uziel Ember, Michael W. Berry, Parallel models of animal migration in Northern Yellowstone National Park, International Journal of Supercomputer Applications and High Performance Computing, 9 (4), 237-255, 1995.

Van den Bosch P. and A. Van der Klauw, Modeling, Identification & Simulation of Dynamical Systems, 1994.
Van Volsem S., W. Dullaert, and H. Van Landeghem, An evolutionary algorithm and discrete event simulation for optimizing inspection strategies for multi-stage processes, European Journal of Operational Research, 179, 621-633, 2007.
Vazquez-Abad F., Sensitivity analysis for stochastic DEDS: An overview, Aportaciones Matematicas, Notas de Investigacion, 7 (2), 163-182, 1992.
Vidal J.-R., and L. Guijarro, A methodology for developing simulation models of ATM networks in SDL language, Computer Communications, 25 (3), 265-287, 2002.
Villen-Altamirano J., Rare event RESTART simulation of two-stage networks, European Journal of Operational Research, 2007.

Wah B., and Y. Chang, Trace-based methods for solving nonlinear global optimization and satisfiability problems, Journal of Global Optimization, 10, 107-141, 1997.
Wakeland W., R. Martin, and D. Raffo, Using Design of Experiments, sensitivity analysis, and hybrid simulation to evaluate changes to a software development process: A case study, Software Process Improvement and Practice, 9, 107-119, 2004.
Walk H., Foundations of stochastic approximation, in Stochastic Approximation and Optimization of Random Systems, Eds. by Ljung L., Pflug G. and Walk H., Birkhauser, Basel, 2-51, 1992.
Walrand J., Network Performance Modeling and Simulation, 1998.
Walsh K., and E. Gu¨n Sirer, Staged simulation: A general technique for improving simulation scale and performance, ACM Transactions on Modeling and Computer Simulation, 14, 170-195, 2004.
Wang J., Contributions to Monte Carlo Analysis: Variance Reduction, Random Search, and Bayesian Robustness, 1994.
Wang P., and D. Chin, Continuous optimization by a variant of simulated annealing, Computational Optimization and Applications, 6 (1), 59-71, 1996.
Wang S., C. Chou, and C. Lin, The design and implementation of the NCTUns network simulation engine, Simulation Modelling Practice and Theory, 15, 57-81, 2007.
Wang Y., T. Putman, N.N. Khrais, C.-R. Chou, G. Kumar, and F. Goh, QoS control for streaming service in UMTS networks, Bell Labs Technical Journal, 11 (4), 185-200, 2007.
Watkins K., Discrete event simulation in C, 1993.
Wei C., Multivariate adaptive stochastic approximation, Annals of Statistics, 15, 1115-1130, 1987.
Whitt W., Minimizing delays in the GI/G/1 queue, Operations Research, 32, 41-51, 1984.
Whitt W., The efficiency of one long run versus independent replications in steady-state simulation, Management Science, 37, 645-666, 1991.
Williamson C., Synthetic traffic generation techniques for ATM network simulations, Simulation, 72, 305-312, 1999.
Wilson W., Simulating Ecological and Evolutionary Systems in C, 2000.
Woods R., and K. Lawrence, Modeling and Simulation of Dynamic Systems, Prentice Hall, 1997.
Wu Z., Software VNA and Microwave Network Design and Characterisation, 2007.
Wu H., Y. Liu, Q. Zhang, and Z.-L. Zhang, SoftMAC: Layer 2.5 collaborative MAC for multimedia support in multihop wireless networks, IEEE Transactions on Mobile Computing, 6 (1), 12-25, 2007.
Wyss G., F. Dura´n, and V. Dandini, An object-oriented approach to risk and reliability analysis: Methodology and aviation safety applications, Simulation, 80, 33-43, 2004.

Xu J., and M. Chung, Predicting the performance of synchronous discrete event simulation, IEEE Transactions on Parallel and Distributed Systems, 15, 1130-1137, 2004.
Xu N., S. Guikema, R. Davidson, L. Nozick, Z. C¸ag?nan, and K. Vaziri, Optimizing scheduling of post-earthquake electric power restoration tasks, Earthquake Engineering and Structural Dynamics, 36, 265-284, 2007.
Xu Q., and C. Tropper, Towards large scale optimistic VLSI simulation, Simulation Modelling Practice and Theory, 14, 695-711, 2006.
Xu Y., S. Sen, and F. Ciarallo, An agent-based data collection architecture for distributed simulations, International Journal of Modelling and Simulation, 24, 55-64, 2004.

Yang G., S. Wang, and R. Wang, An efficient preconditioning technique for numerical simulation of hydrodynamic model semiconductor devices, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 16, 387-400, 2003.
Yaun G., D. Bauer, H. Bhutada, C. Carothers, M. Yuksel, and S. Kalyanaraman, Large-scale network simulation techniques: Examples of TCP and OSPF models, Computer Communication Review, 33, 27-41, 2003.
Yin B., G. Dai, Y. Li, and H. Xi, Sensitivity analysis and estimates of the performance for M / G / 1 queueing systems, Performance Evaluation, 64 (4), 347-356, 2007.
Yu B., and K. Popplewell, Metamodels in manufacturing: A review, International Journal of Production Research, 32, 787-796, 1994.
Yunker J., and J. Tew, Simulation optimization search, Journal of Mathematics and Computers in Simulation, 37 (1), 17-28, 1994.

Zabinsky Z., and R. Smith, Pure adaptive random search in global optimization, Mathematical Programming, 53 (3), 323-338, 1992.
Za´ruba G., I. Chlamtac, and S. Das, A prioritized real-time wireless call degradation framework for optimal call mix selection, Mobile Networks and Applications, 7, 143-151, 2002.
Zeigler B., H. Cho, J. Kim, H. Sarjoughian, and J. Lee, Quantization-based filtering in distributed discrete event simulation, Journal of Parallel and Distributed Computing, 62, 1629-1647, 2002.
Zeigler B., T. Kim, and H. Praehofer, Theory of Modeling and Simulation, 2000.
Zeng Z., and B. Veeravalli, Design and performance evaluation of queue-and-rate-adjustment dynamic load balancing policies for distributed networks, IEEE Transactions on Computers, 55 (11), 1410-1422, 2006.
Zhang C., A. Hammad, T. Zayed, G. Wainer, and H. Pang, Cell-based representation and analysis of spatial resources in construction simulation, Automation in Construction, 16, 436-448, 2007.
Zhang H., C. Tam, and J. Shi, Application of fuzzy logic to simulation for construction operations, Journal of Computing in Civil Engineering, 17, 38-45, 2003.
Zhang Y., H. Qiao, G. Li, and K. Huang, Rollback and persistence study in parallel discrete event simulation, Journal of System Simulation, 19, 67-70, 2007.
Zhang Y., and B. Soong, The effect of handoff dwell time on the mobile network performance, Wireless Personal Communications, 31, 221-234, 2004.
Zhang Y., and M. Fujise, Location management congestion problem in wireless networks, IEEE Transactions on Vehicular Technology, 56, 942-954, 2007.
ZhangY., G. Li, K. Huang, Event and event queue modeling in parallel discrete event simulation, Journal of System Simulation, 19, 1949-1953, 2007.
Zhu Y., M. Ma, and T.H. Cheng, An efficient solution for mitigating light-load penalty in EPONs, Computers and Electrical Engineering, 32 (6), 426-431, 2006.
Zuo Z.-R., X.-F. Shen, and T.-X. Zhang, Infrared scene simulation with image based rendering technique, Infrared and Laser Engineering, 34, 297-300, 2005.

The Copyright Statement: The fair use, according to the 1996 Fair Use Guidelines for Educational Multimedia, of materials presented on this Web site is permitted for non-commercial and classroom purposes only.
This site may be mirrored intact (including these notices), on any server with public access. All files are available at http://home.ubalt.edu/ntsbarsh/Business-stat for mirroring.
Kindly e-mail me your comments, suggestions, and concerns. Thank you.
Professor Hossein Arsham

This site was launched on 4/18/1994, its contents have been updated often, and its entire external links checked once a month ever since.

Back to:

Dr Arsham's Home Page

EOF: Ó 1994-2015.