【光华讲坛】Efficient Simulation Optimization via Optimal Sampling

文:西南财经大学统计学院 发布时间:2017-07-13 浏览次数:1080

题:Efficient Simulation Optimization via Optimal Sampling

主讲人:Chun-Hung Chen,乔治梅森大学教授、IEEE Fellow、国家千人计划特聘专家

主持人:肖辉副教授、博导

间:201771810:30-11:30

点:通博楼B212会议室

主办单位:统计学院、科研处


主讲人简介:

陈俊宏教授入选千人计划国家特聘专家和IEEE Fellow1994年博士毕业于哈佛大学,先后任职于宾夕法尼亚大学、美国乔治梅森大学,现为美国乔治梅森大学系统工程与运筹系教授,2008年至2014年兼任台湾国立大学电机与工业工程系客座教授。先后担任IIE Transactions 编辑(department editor)IEEE Transactions on AutomationScience and EngineeringIEEE Transactions onAutomatic Control等期刊副主编(associate editor),以及其它多个国际期刊(等)编委。主要研究领域:离散事件系统建模与仿真、最优计算量分配,应用于空中交通系统,半导体系统,供应链管理,导弹防御系统及电网等。先后主持美国 NSF, NIH, DOE, NASA, FAA,Missile Defense Agency, and Air Force部门项目多项,著有“Stochastic Simulation Optimization: An Optimal Computing BudgetAllocation”“Stochastic Simulation Optimization for Discrete Event Systems – Perturbation Analysis, Ordinal Optimization, and Beyond”两部专著,在: IEEE Transactions on Automatic ControlAutomaticaInforms Journal on ComputingIIE Transactions等本领域的国际权威期刊上发表论文70余篇。

 

内容提要:

Simulation and optimization are two popular tools in industrial engineering and operations research. Optimization intends to choose the best element from some set of available alternatives. Stochastic simulation is a powerful modeling and software tool for analyzing modern complex systems that arise in manufacturing, power grids, transportation, healthcare, finance, defense, and many other fields. Detailed dynamics of complex, stochastic systems can be modeled in simulation. This capability complements the inherent limitation of traditional optimization, so the combining use of simulation and optimization is growing in popularity.  This seminar discusses the computational issues in such a combination, and presents our effective approaches. A key component of our methodologies is a new technique called Optimal Computing Budget Allocation (OCBA) initially developed by the speaker, which intends to maximize the overall simulation or sampling efficiency for finding an optimal decision.

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