Maria Gisela Bardossy, Ph.D.
Assistant Professor of Decision Science
Department of Information Systems and Decision Science
Office: Business Center 481
- Ph.D., University of Maryland, Robert H. Smith School of Business
- M.S., Clemson University, Department of Mathematical Sciences
- B.S., Universidad Catolica de Cordoba, Facultad de Ingenieria
- Licentiate, Universidad Nacional de Cordoba, Facultad de Administracion
Linear Programming, Dynamic Programming, Heuristics, Telecommunication and Network Design Problems, Simulation Modeling, Agent-based Simulation.
Copyright notice: Since most of these papers are published, the copyright has been transferred to the respective publishers. Therefore, the papers cannot be duplicated for commercial purposes. PDF links are provided solely for the purpose of timely academic dissemination.
- An Inexact Sample Average Approximation Approach for the Stochastic Connected Facility Location Problem, with S. Raghavan, Networks, to appear.
- Approximate Robust Optimization for the Connected Facility Location Problem, with S. Raghavan, Discrete Applied Mathematics, 210, 246-260, 2016.
- Dual-Based Local Search for the Connected Facility Location and Related Problems, with S. Raghavan, INFORMS Journal on Computing, 22(4), 584-602, 2010.
- Communication Networks in Handbook of Discrete and Combinatorial Mathematics with David Simchi-Levi and Sunil Chopra, Chapter 10, Section 10.16, 779-793, 2017.
- Essentials of Linear Programming for Managers: From System of Inequalities to Software Implementation, with H. Arsham, and D. K. Sharma. IGI Global publisher. Chapter 7 (First Edition), 96-127, 2014.
- Analysis of Hump Operation at a Railroad Classification Yard, 493-500, 2015
- Robust Optimization for the Connected Facility Location Problem with S. Raghavan, Electronic Notes in Discrete Mathematics, 44, 149-154, 2013.
Statistics, Decision Science, Linear Programming, Business Analytics.
- OPRE 202: Statistical Data Analysis
- OPRE 315: Business Applications of Decision Science
- OPRE 605: Business Analytics