Maria Gisela Bardossy, Ph.D.
Assistant Professor of Decision Science
- 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
Professor Bardossy joined the Merrick School of Business in 2011 after earning her Ph.D. from the University of Maryland, College Park.
Linear Programming, Dynamic Programming, Heuristics, Telecommunication and Network Design Problems, Simulation Modeling, Agent-based Simulation.
Business Statistics, Operations Research, Simulation, Data Analysis
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. (2017). An Inexact Sample Average Approximation Approach for the Stochastic Connected Facility Location Problem. Networks.
Bardossy, M. G., & Raghavan, S. (2015). Approximate robust optimization for the Connected Facility Location problem. Discrete Applied Mathematics.
Bardossy, M. G. (2015). Analysis of Hump Operation at a Railroad Classification Yard. 493-500.
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).
Contracts, Grants and Sponsored Research
Bardossy, Gisela , "Enhanced Course: OPRE 202" Sponsored by Helen P. Denit Honors Program, The University of Baltimore, $100. (2016).
Bardossy, Gisela , "Enhanced Course: OPRE 315" Sponsored by Helen P. Denit Honors Program, The University of Baltimore, $100. (2016).
Bardossy, Gisela , "Maryland Open Source Textbook Initiative" Sponsored by University of Maryland System, State, $500. (2015).
Bardossy, Gisela , "An Efficient Algorithm for Robust Discrete Optimization" Sponsored by Merrick School of Business Office of the Dean, The University of Baltimore, $3000. (2015).
Bardossy, Gisela , "Integrating Business Analytics into the redesigned MSB MBA" Sponsored by University of Baltimore Foundation - Fund for Excellence Committee, The University of Baltimore, $9000. (2015).
Bardossy, Gisela , "Railroad Classification Yard Scheduling" Sponsored by Merrick School of Business Office of the Dean, The University of Baltimore, $2000. (2014).
Bardossy, Gisela (Principal), "Educational webcasts for Business Statistics and Decision Science" Sponsored by The University of Baltimore, $4755. (2012).
Research in Progress
"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.
"Agent-based Modeling of Dependence Networks" (On-Going)
We live in an interconnected world where agents depend on each other to achieve their personal and group goals. We model this inter-dependency using agent-based simulation and test various hypothesis regarding rules of engagement, dissipation of information in the network, discrepancy between reality and beliefs and the updating and correction of beliefs. This information can inform the design of social platforms as the implications of choice characteristics are better understood.
"Heuristics for the robust prize-collecting steiner tree problem" (On-Going)
We propose a heuristic based on Bertsimas-Sim approach to the prize-collecting steiner tree problem. In the prize-collecting steiner tree problem, we are given a set of customers with potential gains of revenue and the set of edges with fixed installation costs. The goal is to decide which customers to connect to a given root node so that the sum of edge costs plus the node revenues for the nodes that are left out from the solution is minimized. We assume that the revenue and cost have interval uncertainty and propose a modified Bertsimas-Sim heuristic.