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数据魔术师
运筹优化及人工智能系列讲座第37期
【活动信息】
Title: Stochastic Optimization Approaches for Location and Inventory Prepositioning of Disaster Relief Supplies
主 讲 人: Dr. Karmel S. Shehadeh 里海大学助理教授
主 持 人: 程春 东北财经大学助理教授
活动时间: 2022年9月16日 晚上20:00 - 21:30
讲座语言：英文
主办单位：数据魔术师
直播平台：通过数据魔术师粉丝群发布讲座腾讯会议信息
【主讲人简介】
Dr. Karmel S. Shehadeh is an Assistant Professor of Industrial Systems and Engineering (ISE) at Lehigh University. She currently serves as one of the directors of the Operations Research Division at the Institute of Industrial and Systems Engineers. Before joining Lehigh, she was a Presidential and Dean Postdoctoral Fellow at Heinz College of Information Systems and Public Policy at Carnegie Mellon University. She holds a doctoral degree in Industrial and Operations Engineering from the University of Michigan, a master's degree in Systems Science and Industrial Engineering from Binghamton University, and a bachelor's in Biomedical Engineering from Jordan University of Science and Technology.
Shehadeh’s broad methodological research expertise and interests include mixed-integer programming, stochastic optimization, and scheduling theory and algorithms development. Her primary application areas and expertise are in healthcare operations and analytics. Her research group is currently working on solving emerging and challenging real-world optimization problems within and outside healthcare operations. These include healthcare scheduling and capacity planning, home service operations, facility location, and disaster response operations.
【报告摘要】
We consider the problem of preparing for a disaster season by determining where to open warehouses and how much relief item inventory to preposition in each. Then, after each disaster, prepositioned items are distributed to demand nodes during the post-disaster phase, and additional items are procured and distributed as needed. There is often uncertainty in the disaster level, affected areas locations, the demand for relief items, the usable fraction of prepositioned items post-disaster, procurement quantity, and arc capacity. To address uncertainty, we propose and analyze two-stage stochastic programming (SP) and distributionally robust optimization (DRO) models, assuming known and unknown (ambiguous) uncertainty distributions. The first and second stages correspond to pre- and post-disaster phases, respectively. We also propose a model that minimizes the trade-off between considering distributional ambiguity and following distributional belief. We obtain near-optimal solutions of our SP model using sample average approximation and propose a computationally efficient decomposition algorithm to solve our DRO models. We conduct extensive experiments using a hurricane season and an earthquake as case studies to investigate these approaches computational and operational performance.
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