向未來借時間:加速仿真優化



活動地點:校本部東區管理學院實477室

活動時間:2019-09-25 09:30:00

上海管理論壇第398期(陳俊宏教授,美國喬治梅森大學

 

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

人:陳俊宏(Chun-Hung Chen)盛达彩票, 美國喬治梅森大學教授

人:鎮璐盛达彩票,上海大學管理學院教授

    間:2019925日(周三),上午9:30

    點:校本部東區管理學院實477

主辦單位:上海大學管理學院、上海大學管理學院青年教師聯誼會

                    

演講人簡介:

陳俊宏教授、博士、“千人計劃” 國家特聘專家、IEEE Fellow盛达彩票。1994年博士畢業于哈佛大學后,至賓夕法尼亞大學任助理教授盛达彩票,現為美國喬治梅森大學教授盛达彩票,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盛达彩票。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盛达彩票、最優計算量分配,應用于空中交通系統,半導體系統,供應鏈管理盛达彩票,導彈防御系統及電網等。先后主持美國NSF, NIH, DOE, NASA, FAA, Missile Defense Agency, and Air Force部門項目多項盛达彩票,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.

 

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