Data-Driven Chance Constrained Programs over Wasserstein Balls
报告人:Dr. Zhi Chen,City University of Hong Kong 时间:2020年6月10日下午1:30-3:00
报告地点:线下教室526, 报告人线上报告

摘要:Chance constrained programs, which seek for a cost-optimal decision that satisfies a set of uncertain constraints with a pre-specified probability, constitute a popular and versatile method for decision-making under uncertainty. Since the true distribution governing the uncertain problem parameters is typically not known and thus has to be estimated from data, chance constrained programs often suffer from overfitting. In this talk, we study data-driven chance constrained programs that combat the issue of overfitting by hedging against all distributions sufficiently close to the empirical one, where proximity is measured by the Wasserstein distance. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, we provide exact mixed-integer conic programming reformulations. We conclude with numerical results.
报告人简介:Zhi Chen is an Assistant Professor in the Department of Management Sciences, College of Business, City University of Hong Kong. His research interests include (1) decision-making under uncertainty with different levels of data availability and its applications in decision analysis, operations management, and engineering; (2) cooperative game theory for joint activities and its applications in production economics, resource pooling, and risk management. His works have appeared in journals including Management Science, Operations Research, and Transportation Science.