报 告 人：王曙明 教授 （中国科学院大学）
报告时间：2023年12月11日（星期一） 14:00-15:30
报告地点：腾讯会议（线上） 会议ID： 168306919
校内联系人：杨莉 副教授 联系方式：0427-2631255
报告摘要： We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (resp., allows limited) constraint violation for distributions residing (resp., not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type-$1$ Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, \phi-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on the newsvendor problem demonstrate the modeling flexibility and encouraging value of our proposal.
报告人简介：王曙明，中国科学院大学经济与管理学院教授，主要从事不确定性决策与最优化、统计与优化建模、模型不确定性研究及其在选址、物流与供应链管理、交通、健康医疗管理等领域的应用。研究成果分别发表于Production and Operations Management, INFORMS Journal on Computing, Transportation Science, IISE Transactions, Naval Research Logistics, IEEE Trans. Cybernetics等权威杂志上。目前担任运筹学著名期刊《Computers & Operations Research》的领域主编 (Area Editor)，以及决策科学旗舰期刊《Decision Sciences Journal》的副主编 (Associate Editor).
