报告题目：Data-Driven Supply-Network Design: Robustness and Latent-Covariate Effects
内容摘要：We investigate a two-stage supply-network design problem with random demands from multiple customers, where the decision maker needs to decide which production facilities to open and select emergency supplier (in case of demand shortage) for each customer (first stage) to collectively fulfill the demand (second stage), so as to minimize the expected total cost. We consider the problem in a data-driven setting, where historical information on demand and some covariates are accessible. In practice, determinants for the multi-dimensional random demand over the supply network are usually various, complicated, and even unobservable, which can hardly be well characterized by the observable historical demand and covariates available. To address the demand ambiguity and the underlying unobservable demand effects in a statistically coherent fashion, we develop a predictive distributionally robust approach that incorporates into the ambiguity set a general factor model for capturing both explanatory effects of observable and latent covariates for the demand. We identify an implied asymptotic decision-dependent worst-case distribution, which facilitates our operational analysis on the production and emergency supplies, and leads to a reformulation of mixed-integer conic program for the problem. We also derive finite-sample and asymptotic performance guarantees of the model solution under several statistical regularity conditions. Finally, sufficient experiments using real-life regional demand data with macroeconomic covariates demonstrate the effectiveness of our framework.
主讲人简介：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 等权威杂志上。目前担任期刊Computers & Operations Research领域主编 (Area Editor)，Decision Sciences副主编(Associate Editor)。
报告时间：2024年03月05日(周二)，16:30-18:00
线下地点：厦门大学经济楼N302
线上地点：腾讯会议 ID：662 6149 3785
