讲座题目：Asymptotically Optimal Lagrangian Policies for Multi-Warehouse, Multi-Store 

                 Systems with Lost Sales

主讲人：缪森涛 助理教授

主持人：晏鹏宇 教授

时间：2022年4月2日 (周六)     上午10:00

在线讲座平台及会议ID：腾讯会议 428 631 408

讲座简介：

We consider a periodic-review inventory control problem for the Multi-Warehouse Multi-Store system with lost sales. We focus on a time horizon during which the system receives no external replenishment. Specifically, each warehouse has a finite initial inventory at the beginning of the horizon, which is then periodically allocated to the stores in each period in order to minimize the total expected lost-sales costs, holding costs, and shipping costs. This is a hard problem and the structure of its optimal policy is extremely complex. We develop simple heuristics based on Lagrangian relaxation that are easy to compute and implement, and have provably near-optimal performances. In particular, we show that the losses of our heuristics are sublinear in both the length of the time horizon and the number of stores. This improves the performance of existing heuristics in the literature whose losses are only sublinear in the number of stores. Numerical study shows that the heuristics perform very well. We also extend our analysis to the setting of positive delivery lead times.

主讲人简介：

Sentao Miao is an Assistant Professor in Bensadoun School of Retail Management & Desautels Faculty of Management at McGill University. His research interests are mainly in developing efficient learning and optimization algorithms with various applications in Operations Management. For methodologies, Sentao Miao focuses on statistical and machine learning algorithms such as online learning, multi-arm bandit problem, reinforcement learning; he is also interested in approximation algorithms with provable performance. For applications, he mainly works on operations management problems such as dynamic pricing, assortment selection, inventory control, etc. Sentao Miao obtained his PhD degree in Department of Industrial and Operations Engineering at University of Michigan.

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