西安交通大学管理学院      管理科学前沿讲座

讲座题目：Data-Driven Reliable Facility Location Design

讲座专家：沈浩 助理教授

时间：2023.12.12 14:00

地点：管理学院311会议室

主办：管理科学系

摘要：We study the reliable (uncapacitated) facility location(RFL) problem in a data-driven environment where historical observations of random demands and disruptions are available. Owing to the combinatorial optimization nature of the RFL problem and the mixed-binary randomness of parameters therein, the state-of-the-art RFL models applied to the data-driven setting either suggest overly conservative solutions, or become computationally prohibitive for large- or even moderate-size problems. In this paper, we address the RFL problem by presenting an innovative prescriptive model aiming to balance solution conservatism with computational efficiency. In particular, our model selects facility locations to minimize the fixed costs plus the expected operating costs approximated by a tractable data-driven estimator, which equals to a probabilistic upper bound on the intractable Kolmogorov distributionally robust optimization estimator. The solution of our model is obtained by solving a mixed-integer linear program that does not scale in the training data size. Our approach is proved to be asymptotically optimal, and offers a theoretical guarantee for its out-of-sample performance in situations with limited data. In addition, we discuss the adaptation of our approach when facing data with covariate information. Numerical results demonstrate that our model significantly outperforms several important RFL models with respect to both in-sample and out-of-sample performances as well as computational efficiency.

个人简介：沈浩，中国人民大学商学院助理教授。主要以数据分析和运筹优化理论为基础，聚焦供应链不确定性风险的分析与应对、以及供应链网络优化的算法设计与决策工具的开发进行研究。主持国家自然科学基金项目2项，多项研究成果发表在运筹学与管理科学领域顶级期刊Manufacturing & Service Operations Management, Production & Operations Management, INFORMS Journal on Applied Analytics上，其中与京东合作的物流配送网络智能化布局项目成果在国家自然科学基金委网站上进行了专题报道。

信息来源：http://som.xjtu.edu.cn/info/1056/10226.htm