Predictive Prescriptions in Distributionally Robust Optimization

Shuming Wang（王曙明）

<p>We investigate several aspects of predictive prescriptions in data-driven distributionally robust optimization (DRO), including statistical modeling, performance guarantees of prescriptions, and tractability. In particular, we are trying to answer the following questions: (i) How to capture the correlation/unobservable effects in the data-driven DRO models? (ii) How to incorporate the time-series forecasts in the multi-stage DRO models? (iii) How to capture jointly the ambiguity and misspecification effects in the DRO framework.</p>

2023-10-17 (Tuesday) 16:30-18:00

经济楼D136（线下分会场），腾讯会议 ID：39337743329

中文

中国科学院大学经济与管理学院、中国科学院预测科学研究中心、厦门大学邹至庄经济研究院、NSFC“计量建模与经济政策研究”基础科学中心

系列讲座

洪永淼

<p>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 等权威杂志上。目前担任期刊Computers &amp; Operations Research领域主编 (Area Editor)，Decision Sciences副主编(Associate Editor)。</p>

“邹至庄讲座”青年学者论坛（第57期）