ABSTRACT

Ordinal Optimization (OO) has emerged as an efficient technique for simulation and optimization. Exponential convergence rates can be achieved in many cases. Optimal Computing Budget Allocation (OCBA) is developed to maximize the computational efficiency by determining the optimal computing resource allocation among a fixed set of simulated designs, no matter the goal is to find the best design and a subset of good designs. OCBA can further enhance the efficiency of OO for stochastic simulation optimization. In this talk, we will present the basic idea of OCBA and its recent development. By integrating with search methods such as Nested Partition scheme invented by Prof. Shi, OCBA can effectively handle large-scale simulation optimization.

BIOGRAPHICAL SKETCH

Chun-Hung Chen received his Ph.D. degree in Engineering Sciences from Harvard University in 1994 and is currently a Professor of Systems Engineering & Operations Research at George Mason University. Dr. Chen was an Assistant Professor of Systems Engineering at the University of Pennsylvania before joining GMU. His research interests are mainly in development of very efficient methodology for stochastic simulation optimization and its applications to air traffic management, rail transportation, semiconductor manufacturing, healthcare, supply chain management, and missile defense system. Dr. Chen received the Best Automation Paper Award from the 2003 IEEE International Conference on Robotics and Automation, 1994 Eliahu I. Jury Award from Harvard University, and the 1992 MasPar Parallel Computer Challenge Award. Dr. Chen has served as Co-Editor of the Proceedings of the 2002 Winter Simulation Conference and Program Co-Chair for 2007 Informs Simulation Society Workshop.  He is currently a simulation department editor for IIE Transactions, associate editor of IEEE Transactions on Automatic Control, area editor of Journal of Simulation Modeling Practice and Theory, and associate editor of International Journal of Simulation and Process Modeling.