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服務(wù)科學(xué)與運(yùn)營(yíng)管理學(xué)系學(xué)術(shù)講座

【主講題目】

Distributionally Robust Optimization Under Stochastic Disruptions

【主講人】楊浩翔，香港中文大學(xué)（深圳） 助理教授

講座信息

時(shí)間：2021年12月3日（周五）14:00-16:00

參與方式：騰訊會(huì)議（ ID：125 318 244）

主持人：張政，浙江大學(xué)管理學(xué)院研究員

主講人簡(jiǎn)介

Haoxiang Yang is an assistant professor in the School of Data Science at the Chinese University of Hong Kong, Shenzhen. His current research interests include optimization under uncertainty, large-scale optimization and decomposition methods, mixed integer programming, and nonlinear optimization, with applications focusing on energy systems, disaster relief and humanitarian aid, public health, air transportation, supply chain, and sports analytics. Prior to joining CUHK-Shenzhen in Summer 2021, Professor Yang was a Postdoctoral Research Associate of Los Alamos National Laboratory, working on nonlinear optimization under uncertainty models and algorithms for power system contingency analysis, quantum computing, and pandemic control.

講座摘要

A stochastic disruption is a type of infrequent event in which the timing and the magnitude are random. We introduce the concept of stochastic disruptions, and a stochastic optimization framework is proposed for such problems. In this talk, we discuss two possibilities where we do not know the exact probabilistic distribution of the uncertainty under the stochastic disruption setting, one for the uncertainty magnitude and the other for the timing. We propose stochastic programming models for each case and solve them using cutting-plane methods. We present computational results of our approach applied to an optimal power flow problem with N-1 contingencies.

信息來(lái)源：浙大管院服務(wù)科學(xué)與運(yùn)營(yíng)管理學(xué)系

今日編輯/排版：段婷

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