題    目：向未來借時間：加速仿真優化Borrowing Time from the Future to Accelerate Simulation-based Optimization

演 講 人🧑🏿‍🚀：陳俊宏（Chun-Hung Chen）， 美國喬治梅森大學教授

主 持 人🛍️：鎮璐，意昂2教授

時    間：2019年9月25日（周三），上午9:30

地    點：校本部東區意昂2官网實477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 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.

歡迎廣大師生參加！