题    目：向未来借时间：加速仿真优化Borrowing Time from the Future to Accelerate Simulation-based Optimization

演 讲 人：陈俊宏（Chun-Hung Chen）， 美国乔治梅森大学教授

主 持 人：镇璐，上海大学管理学院教授

时    间：2019年9月25日（周三），上午9:30

地    点：校本部东区管理学院实477室

主办单位：上海大学管理学院、上海大学管理学院青年教师联谊会

演讲人简介：

陈俊宏教授、博士、IEEE Fellow。1994年博士毕业于哈佛大学后，至宾夕法尼亚大学任助理教授，现为美国乔治梅森大学教授，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。为IEEE Transactions on Automation Science and Engineering、IEEE Transactions on Automatic Control等期刊副主编，以及其它多个国际期刊（IIE Transactions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 Budget Allocation"等两部专著，在本领域重要国际期刊论文多篇。

演讲内容简介：

Simulation can model the complexity and uncertainty of modern systems. This capability complements the inherent limitation of traditional optimization, so the combining use of simulation and optimization is growing in popularity. While the advance of new technology has dramatically increased computational power, efficiency is still a concern for simulation-based optimization. Optimal Computing Budget Allocation (OCBA) initially developed by the speaker can dramatically enhance simulation efficiency. Its idea is to maximize the overall computational efficiency for finding an optimal decision. To further cut short the time to reach a good decision, we propose a concept of borrowing time from the future and develop a two-phase framework: i) Pre-event look-ahead simulation-driven learning: Before a decision point, we generate look ahead data by smartly simulating some future functioning scenarios, and discover the distribution of the optimal policy from simulated decisions; ii) Post-event fast-time decision: At the decision point, our innovative synthesizer efficiently utilizes look-ahead simulation learning and additional minimum new simulations to quickly offer optimal actions. This new framework enables fast-time simulation-based decision making.

欢迎广大师生参加！