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.

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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 ## A Basic Scientific Calculator

General Resources

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 systčmes 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 SitesGeneral 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 & OrganizationsNational 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 SocietyOrganizations:

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

## ACM Transactions on Modeling and Computer Simulation

Journal WebSites

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 ArticlesAbate J., and W. Whitt, Transient behavior of regular Brownian motion, I and II,

Advance Applied Probability19, 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 VersionAsmussen 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, 515-526Bandyopadhyaya 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 -29Bekey 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, 1-38Bhaté-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, 529-551Cao 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, 1997Chen 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, H, 493-496, 1996, (alternative convergence conditions for SPSA).^{th}IFAC World CongressChen M-H., and B. Schmeiser, Performance of the Gibbs, hit-and-run, and Metropolis samplers,

Journal of Computational and Graphical Statistics2, 1993, 251-272Cheng 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, 572-590Choi 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, 1998Clark 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, 1995Clymer 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, 285-295Davies 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, 391-413Desrochers 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, 1991Frater 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- 138Garcia-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.Jonsbrĺten 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, 1346-1353Kiefer 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 Research75, 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, 1995Kuk, 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, 1994L'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, 1069-1070, 1996.^{th}IEEE Conference on Decision and ControlLee 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, 1992Legato 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.Lřvĺs 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, 1988Robbins 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, 1995Sanchez 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, 129-134Shi 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 theWinter 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, 1982Cairoli 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, 1989Dagpunar 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, 1978Fishman G.,

Concepts and Methods in Discrete Event Digital simulation, Wiley, 1973Fishwick 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, 1978Ho 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, 1989Hoppensteadt 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, 1994Kitamura 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, 1992Kleinrock 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/lkbookLewis P., and E. Orav,

Simulation Methodology for Statisticians, Operations Analysts, and Engineers, Wadsworth Inc., 1989Lindemann 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, 1982Mikhailov 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, 1994Van 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, 1992Vose 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, 1984Zeigler 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.Binder K., and D. Heermann,

Monte Carlo Simulation in Statistical Physics: An Introduction, 1998.Bumble M., and L. Coraor, An architecture for a nondeterministic distributed simulator,

Burke Speech Processing for IP Networks: Media Resource Control Protocol, 2007.Chung C.,

Simulation Modeling Handbook: A Practical Approach, 2003.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.Davison A. , and D. Hinkley,

Bootstrap Methods and their Application, 1997.Delaney W., and E. Vaccari,

Dynamic Models and Discrete Event Simulation, 1989.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.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.Friedman L.,

The Simulation Metamodel, 1996.Fu M., Optimization via simulation: A review,

Annals of Operations Research, 53, 199-247, 1994.Fujimoto R.,

Parallel and Distributed Simulation Systems, 2001Richard M.,

Futschik A., and G. Pflug, Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms,Fujimoto Network Simulation (Synthesis Lectures on Communication Networks), 2006.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,

Gala´n-Mari´n G., E. Me´rida-Casermeiro, and J. Mun~o-Pe´rez, Modelling competitive Hopfield networks for the maximum clique problem,Lect. Notes Econ. Math. Syst., 374, 24-44, 1992.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.Hill D.,

Object-Oriented Analysis and Simulation Modeling, 1996.Hybinette M., and R. Fujimoto, Latency hiding with optimistic computations,

Computer Networks and ISDN Systems, 26, 1447-1456, 1994.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.Janke W., Introduction to simulation techniques,

Lecture Notes in Physics, 207-260, 2007.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.Kleijnen J., and W. Van Groenendaal,

Simulation: A Statistical Perspective, 1992.Kouikoglou V., and Y. Phillis,

Hybrid Simulation Models of Production Networks, 2001.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.Law A., and W. Kelton,

Simulation Modeling and Analysis, 1999.Leemis L., and S. Park,

Discrete-Event Simulation: A First Course, 2005.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.MacKeown P.,

Stochastic Simulation in Physics, Springer, New York, 1997.Michel Mandjes,

Large Deviations for Gaussian Queues: Modelling Communication Networks, 2007.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.Nelson B.,

Stochastic Modeling: Analysis & Simulation, 1995.

Neurocomputing, 58-60, 1117-1122, 2004.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.Orsoni A., Fuzzy and simulation-based techniques for industrial safety and risk assessment,

International Journal of General Systems35 (5), 619-635, 2006.Pidd M.,

Computer Simulation in Management Science, 1998.Polak E.,

Optimization: Algorithms and Consistent Approximations, 1997.Pooch U., and J. Wall,

Discrete Event Simulation: A Practical Approach, 1993.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.Quaglia F., A restriction of the elastic time algorithm,

Information Processing Letters, 83, 243-249, 2002.Rardin R.,

Optimization in Operations Research, 1998.Reeves C., and J. Rowe,

Genetic Algorithms: Principles and Perspectives, 2002.Romeijn H.,

Global Optimization by Random Walk Sampling Methods, 1992.Rubinstein R, and Melamed B.,

Modern Simulation and Modeling, 1998.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.Saviotti P.,

Applied Evolutionary Economics: New Empirical Methods and Simulation Techniques, 2002.Severance F.,

System Modeling and Simulation: An Introduction, 2001.Sheluhin O., S. Smolskiy, and A. Osin,

Self-Similar Processes in Telecommunications, 2007.Sichman J., R. Conte, and N. Gilbert,

Multi-Agent Systems and Agent-Based Simulation, 1998.Silberschatz A., P. Galvin, and G. Gagne,

Operating System Concepts, 2004.Simonoff J.,

Smoothing Methods in Statistics, 1996.Singhal S., and M. Zyda,

Networked Virtual Environments: Design and Implementation, 1999.Szczerbicka H., and P. Ziegler, Simulation with active objects: an approach to combined modeling,

Simulation Practice and Theory, 1, 267-281, 1994.Tarasenko G.,

Stochastic Optimization in the Soviet Union, 1986.Tyrtyshnikov E.,

A Brief Introduction to Numerical Analysis, 1997.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.Villen-Altamirano J., Rare event RESTART simulation of two-stage networks,

European Journal of Operational Research, 2007.Walrand J.,

Network Performance Modeling and Simulation, 1998.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.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.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 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.Yunker J., and J. Tew, Simulation optimization search,

Journal of Mathematics and Computers in Simulation, 37 (1), 17-28, 1994.Zeigler B., T. Kim, and H. Praehofer,

Theory of Modeling and Simulation, 2000.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.

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