Latest Developments in Optimal Computing Budget Allocation for Simulation Based Optimization

摘要：

We consider the simulation based optimization problem where the number of alternatives is finite. Simulation is used to estimate the performance measures of each alternative when analytical expression is too complex or even unavailable. As multiple replications are required for each design, there is a need to efficiently allocate the simulation budget. The Optimal Computing Budget Allocation (OCBA) is one kind of simulation budget allocation approach and has demonstrated its ability in significantly enhancing simulation efficiency. It intelligently allocates simulation budget for maximizing the desired selection quality in finding the best alternative (s) under computing budget constraint. In this paper, we specifically present three latest developments on OCBA which handle the optimal subset selection, constrained optimization, and multi-objective optimization problems. The computing budget allocation models for these three problems are built. Based on these models, we derive the corresponding asymptotically optimal allocation rules. Some numerical results are provided to show the efficiency of rules derived from the optimal computing budget allocation framework.

关键词： simulation optimization ranking and selection optimal computing budget allocation

作者: Loo Hay Lee Chun-Hung Chen Ek Peng Chew Si Zhang Juxin Li Nugroho Artadi Pujowidianto

作者单位: Department of Industrial and Systems Engineering,National University of Singapore,Singapore Department of Systems Engineering and Operations Research,George Mason University,USA Department of Department of Industrial and Systems Engineering, National University of Singapore,Singapore Department of Industrial and Systems Engineering,National University of Singapore, Singapore

会议类型: 国际会议

会议名称: The Institute Industrial Engineera Asian Conference 2011(2011年国际工业工程师协会亚洲会议)

会议地点: 上海

会议语种:英文

页码: 59-67

在线出版日期: 2011-06-10（万方平台首次上网日期，不代表论文的发表时间）