Assistant Professor of Decision Science
Department of Information Systems and Decision Science
Office: Business Center 481
Professor Bardossy joined the Merrick School of Business in 2011 after earning her Ph.D. from the University of Maryland, College Park.
Arsham, H., Bardossy, G., & Sharma, D. K. (2014). Essentials of Linear Programming for Managers: From System of Inequalities to Software Implementation. IGI Global publisher. Chapter 7( First Edition), 96-127.
Refereed Journal Articles
Bardossy, G., & Raghavan, S. (2010). Dual-Based Local Search for the Connected Facility Location and Related Problems. INFORMS Journal of Computing. 22(4), 584-602.
Bardossy, G., & Raghavan, S. (2013). Robust Optimization for the Connected Facility Location Problem. Electronic Notes in Discrete Mathematics. 44. 149-154.
Bardossy, G. INFORMS Telecommunications Conference, "Robust Optimization for the Connected Facility Location Problem," Lisboa, Portugal. (2014).
Bardossy, G. Decision Science Institute, "Tips and Experiences from Efforts to Improve the Statistics Class," Baltimore. (2013).
Bardossy, G. Regional Undergraduate Mathematics Research Conference, "The Mathematics of YouTube: Where Exactly on the Internet is the Gangnam Style Video?," Mathematical Association of America, Townson University. (2013).
Bardossy, G. INFORMS Annual Meeting, "Stochastic Dynamic Allocation of Deceased-Donor Kidneys Based on Historical Data Logs," Austin, TX. (2010).
Bardossy, G. INFORMS Annual Meeting, "The Connected Facility Location Problem under Customer Uncertainty," Austin, TX. (2010).
Bardossy, G. The 10th INFORMS Telecommunications Conference, "The Stochastic Connected Facility Location Problem," Montreal, Canada. (2010).
Bardossy, Gisela (Principal), "Educational webcasts for Business Statistics and Decision Science" Sponsored by The University of Baltimore, $4755. (2012).
"A Compact Formulation for Robust Optimization versus A Bertsimas and Sim Heuristic Method" (On-Going)
Location problems arise in many applications and have received significant attention from the OR community. Optimization under uncertainty and in particular robust optimization is relevant in location applications when there incomplete information. In this papers, we compare two very different strategies for robust optimization in a family of location problems: a compact formulation that seeks to achieve an optimal solution in a holistic formulation, and a sequential and fractional formulation within a heuristic.
"Stochastic Dynamic Allocation of Deceased - Donor Kidneys Based on Historical Data Logs"" (Writing Results)
"The Connected Facility Location Problem under Customer Uncertainty" (Writing Results)