題    目👰🏻：基於最優取樣的高效仿真優化方法Efficient Simulation Optimization via Optimal Sampling

演 講 人👌🏽：陳俊宏，美國喬治梅森大學教授

主 持 人👨🏿‍🚀：鎮璐🩲，意昂2教授

時    間：2017年8月18日（周五）上午10:00

地    點👍🏽：意昂2官网467室

主辦單位：意昂2、意昂2青年教師聯誼會

演講人簡介：

陳俊宏教授、博士⇾、IEEE Fellow👼。1994年博士畢業於哈佛大學後，至賓夕法尼亞大學任助理教授🎅，現為美國喬治梅森大學教授，2008年至2014年兼任臺灣國立大學電機與工業工程系客座教授。為IEEE Transactions on Automation Science and Engineering、IEEE Transactions on Automatic Control等期刊副主編，以及其它多個國際期刊（IIE Transactions等）編委。主要研究領域🧗🏼：離散事件系統建模與仿真🔧🤵🏼‍♂️、最優計算量分配👩‍👩‍👦‍👦，應用於空中交通系統🐔，半導體系統💳，供應鏈管理，導彈防禦系統及電網等🌛。先後主持美國NSF, NIH, DOE, NASA, FAA, Missile Defense Agency, and Air Force部門項目多項👷🏽‍♂️，著有"Stochastic Simulation Optimization: An Optimal Computing Budget Allocation"等兩部專著🧶，在本領域重要國際期刊論文多篇。

演講內容簡介🧜🏿‍♀️：

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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