Volume 54, Issue 1 Contributors

Belarmino Adenso-Diaz (“Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search”) is a Professor in the Engineering School, University of Oviedo, Spain. His experience is that fine-tuning algorithms is one of the most tedious tasks in the development and implementation of optimization procedures. The idea for this paper surfaced in conversations about how to use exiting operations research tools to facilitate building additional ones.

Frederic Babonneau (“Solving Large Scale Linear Mulicommodity Flow Problems with an Active Set Strategy and Proximal-ACCPM”) is a Ph.D. student at the University of Geneva. He is interested in developing solution methods for large scale convex optimization problems. The present work is part of his Ph.D. thesis which is devoted to solving linear and nonlinear multicommodity flow problems using the analytic center cutting plane method.

Erin Baker (“Increasing Risk and Increasing Informativeness: Equivalence Theorems”) is an Assistant Professor in the Department of Mechanical and Industrial Engineering at University of Massachusetts, Amherst. She is interested in decision making under uncertainty, with applications to climate change policy and technology R&D. This research was part of her Ph.D. thesis, conducted at Stanford University under the supervision of John Weyant, James Sweeney, Susan Athey, and Jonathon Levin.

Dimitris Bertsimas (“A Robust Optimization Approach to Inventory Theory”) is the Boeing Professor of Operations Research at the Sloan School of Management and the Operations Research Center at the Massachusetts Institute of Technology. His current research focuses on robust optimization as a tractable theory for optimization under uncertainty.

Qing Ding (“Dynamic Pricing through Customer Discounts for Optimizing Multi-Class Customers Demand Fulfillment”) is an Assistant Professor of Operations Management at the School of Business, Singapore Management University. The paper is part of a dissertation written under the direction of Prof. Panos Kouvelis and Prof. Joseph Milner.

Oliver du Merle (“Solving Large Scale Linear Mulicommodity Flow Problems with an Active Set Strategy and Proximal-ACCPM”) is the Operational Research Director of Air France. He holds a Ph.D. in Operations Research from the University of Geneva. He has contributed to new methods for column generation schemes and is interested in applying them to airline problems.

Peter W. Glynn (“A Nonparametric Approach to Multi-Product Pricing”) is the Thomas Ford Professor of Engineering in the Department of Management Science and Engineering at Stanford University, and has a courtesy appointment in the Department of Electrical Engineering. He is a Fellow of the Institute of Mathematical Statistics. His research interests include computational probability, queuing theory, statistical inference for stochastic processes, and stochastic modeling.

Linda V. Green (“Managing Patient Service in a Diagnostic Medical Facility”) is the Annand G. Erpf Professor of Business at the Graduate School of Business, Columbia University. This work is a part of a series of papers focusing on applications of operations research in the healthcare industry. Her current research focuses on improving emergency responsiveness and identifying strategies for the effective design and management of diagnostic facilities.

Ahmed Hadjar (“A Branch-and-Cut Algorithm for the Multiple Depot Vehicle Scheduling Problem”) is a researcher at the École Polytechnique de Montréal and the Groupe d'Études et de Recherche en Analyse des Décisions (GERAD). He received a Ph.D. in operations research from the Institut National Polytechnique de Grenoble (France). His research interests are in combinatorial optimization and integer programming, especially applications to routing and scheduling.

L. Jeff Hong (“Discrete Optimization via Simulation using COMPASS”) is an Assistant Professor in the Department of Industrial Engineering and Engineering Management at the Hong Kong University of Science and Technology. His research interests include optimization via simulation, simulation experimental design and simulation output analysis. This paper is based on a chapter in his Ph.D. dissertation.

Panos Kouvelis (“Dynamic Pricing through Customer Discounts for Optimizing Multi-Class Customers Demand Fulfillment”) is the Emerson Distinguished Professor of Operations and Manufacturing Management, and Director of the Boeing Center on Technology, Information and Manufacturing, at the Olin School of Business, Washington University. His research interests include global supply chain management, dynamic pricing and revenue management, integrated risk management in supply chains, global business process outsourcing, operations strategy. This is part of ongoing research efforts on real time pricing and inventory policies for agile supply chains with his research collaborators professors, Joe Milner and Qing Ding.

Manuel Laguna (“Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search”) is a Professor at the Leeds School of Business, University of Colorado at Boulder. He has done extensive research in the development and application of metaheuristic methods for difficult optimization problems. Developing a systematic way for fine-tuning optimization procedures based on metaheuristic technology was the motivation for the research that is described in the paper.

Odile Marcotte (“A Branch-and-Cut Algorithm for the Multiple Depot Vehicle Scheduling Problem”) is a Professor in the Department of Computer Science at the Université du Québec a Montréal. She received a Ph.D. in operations research from Cornell University. Her main research interests are in combinatorial optimization and integer programming, especially problems arising in transportation and telecommunication. She is also a member of the Groupe d'Études et de Recherche en Analyse des Décisions (GERAD).

Joseph A. Milner (“Dynamic Pricing through Customer Discounts for Optimizing Multi-Class Customers Demand Fulfillment”) is an Assistant Professor of Operations Management at the University of Toronto. His research interests include supply chain structures and contracts, dynamic pricing and revenue management, and production and inventory management.

Barry L. Nelson (“Discrete Optimization via Simulation using COMPASS”) is the James N. and Margie M. Krebs Professor of Industrial Engineering and Management Sciences at Northwestern University, and is Director of the Masters of Engineering Management Program there. His research centers on the design and analysis of computer simulation experiments on models of stochastic systems. This paper is part of his long-term program to create general-purpose, and practically useful, tools for the comparison of alternative system designs via simulation.

R. Ravi (“Approximation Algorithms for Problems Combining Facility Location and Network Design”) is a Professor of Operations Research and Computer Science at the Tepper School of Business, Carnegie Mellon University. His interests lie mainly in Approximation Algorithms, and more broadly in Combinatorial Optimization and Computational Biology. This paper is part of the doctoral dissertation of his student, Amitabh Sinha, and is also closely tied to the themes of an NSF funded exploration on the same topic under the ALADDIN project at CMU co-organized by R. Ravi.

Paat Rusmevichientong (“A Nonparametric Approach to Multi-Product Pricing”) is an Assistant Professor in the School of Operations Research and Industrial Engineering at Cornell University. His research interests include data mining and decision-making in complex systems. The paper was motivated by his summer internship experience at General Motors, and was part of the dissertation written under the direction of Professor Benjamin Van Roy at Stanford University.

Sergei L. Savin (“Managing Patient Service in a Diagnostic Medical Facility”) is an Associate Professor of Decision, Risk, and Operations at the Graduate School of Business, Columbia University. Savin's research interests include stochastic models of service systems, revenue management, and marketing/operations coordination in the area of new product development.

Suvrajeet Sen (“Stochastic Programming Approach to Power Portfolio Optimization”) is a Professor Systems and Industrial Engineering at the University of Arizona. Professor Sen's research interests are in the area of optimization algorithms, especially those arising in the context of stochastic programming, integer programming, and their applications in energy, telecommunications, and transportation modeling. This paper represents an effort to integrate our algorithmic research on stochastic programming, with statistical and computational techniques to provide a comprehensive solution to an important portfolio optimization problem arising in electricity markets.

Amitabh Sinha (“Approximation Algorithms for Problems Combining Facility Location and Network Design”) is an Assistant Professor of Operations and Management Science at the Stephen M. Ross School of Business, University of Michigan. This paper is one chapter of his doctoral dissertation written under the direction of R. Ravi at Carnegie Mellon University. The dissertation dealt with various aspects of location modeling such as location under uncertainty, competitive location, and multi-commodity facility location. His current research interests include the impact of social networks in organizational behavior and the presence and magnitude of first-mover disadvantages in the market positioning of new products.

Francois Soumis (“A Branch-and-Cut Algorithm for the Multiple Depot Vehicle Scheduling Problem”) is a Professor in the Department of Mathematics and Industrial Engineering at the École Polytechnique de Montréal. He is also a member of the Groupe d'Études et de Recherche en Analyse des Décisions (GERAD). His main research interests include large-scale optimization for vehicle routing and crew scheduling in air, rail, and urban transportation.

Gene Talat (“Stochastic Programming Approach to Power Portfolio Optimization”) is a Professor at the Genc Department of Economics University of Guelph, Canada. Professor Genc' s research interests are in energy economics, industrial organization, and econometrics, especially in the context of problems arising the electric power industry. He is also interested in modeling uncertainty within game theoretic models. The statistical and econometric aspects of this paper were developed as parts of his dissertation at the University of Arizona.

Aurélie Thiele (“A Robust Optimization Approach to Inventory Theory”) is an Assistant Professor in the Department of Industrial and Systems Engineering at Lehigh University. Her research focuses on the development and analysis of tractable models of uncertainty for dynamic optimization problems in operations management. This work was part of her Ph.D. dissertation at the Massachusetts Institute of Technology under the supervision of Dimitris Bertsimas.

Benjamin Van Roy (“A Nonparametric Approach to Multi-Product Pricing”) is an Assistant Professor of Management Science and Engineering, Electrical Engineering, and by courtesy, Computer Science at Stanford University. His recent research interests include stochastic control, machine learning, economics, finance, and information technology.

Jean-Philippe Vial (“Solving Large Scale Linear Mulicommodity Flow Problems with an Active Set Strategy and Proximal-ACCPM”) is a Professor of Operations Management at the University of Geneva. He is interested in algorithms for convex optimization and their applications to logistics.

Ben Wang (“Managing Patient Service in a Diagnostic Medical Facility”) holds a Ph.D. in Decision, Risk, and Operations from Columbia Business School. He previously worked as an associate at the Industrial & Commercial Bank of China and is currently with Century Securities of China.

Ward Whitt (“Fluid Models for Multiserver Queues with Abandonments”) is a Professor at Columbia University in the Department of Industrial Engineering and Operations Research. He joined the faculty of Columbia University after spending 25 years in research at AT&T.

Lihua Yu (“Stochastic Programming Approach to Power Portfolio Optimization”) is a Quantitative Analyst at Pennsylvania Power and Light (PPL). Dr. Lihua Yu's research interests are in applications of operations research and related quantitative tools to problems arising in the electric power industry. Prior to joining PPL, he was a Research Associate at the State Utility Forecasting Group at Purdue University. The optimization algorithm reported in this paper was part of Dr. Yu's dissertation research at the University of Arizona.