Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models
主讲人：覃含章 博士候选人（美国麻省理工学院MIT）
主持人：周伟华 教授 （yl23411永利）
时间：2019年7月8日 星期一 上午9:00-10:30
地点：浙江大学紫金港校区行政楼yl23411永利1002会议室
摘 要：
We study the classic multi-period joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of an item that she wishes to sell. The retailer's objective is to maximize the expected profit over a finite horizon, by matching the inventory level with a random demand that depends on the price in each period. In reality, the demand functions or the distribution of the random noise are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm, which uses the past demand data to solve the joint pricing and inventory control problem. We assume the retailer does not know the noise distributions or the true demand functions; instead she only has access to demand hypothesis sets that contain the true demand functions. We prove the algorithm's sample complexity bound, the number of data samples needed to guarantee a near-optimal profit, is O(T6 ε−2 logT), where T is the number of periods and ε is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. A simulation study suggests that the data-driven algorithm solves the dynamic program effectively.
主讲人简介：
Hanzhang Qin is currently a Ph.D. candidate in Computational Science and Engineering, advised by Professor David Simchi-Levi. His primary research interests include data-driven multi-stage stochastic systems, and especially applications of machine learning techniques to revenue management. He holds a dual S.M. degree in EECS and transportation from MIT and a dual B.S. degree in Mathematics and Industrial Engineering from Tsinghua University.
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