报告题目： MNL选择模型下的选品与库存联合在线优化
报 告 人： 梁 湧 清华大学
报告时间：2023年11月8日(周三) 16:00-17:30
报告地点：中国科学院大学中关村校区教学楼S406
腾讯会议：816-5319-3902
内容摘要
We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the realized demands about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effectively balance the exploration and exploitation in the online decision-making of assortment and inventory. Our algorithm builds on a new estimator for the MNL attraction parameters, a novel approach to incentivize exploration by adaptively tuning certain known and unknown parameters, and an optimization oracle to static single-cycle assortment-inventory planning problems with given parameters. We establish a regret upper bound for our algorithm and a lower bound for the online joint assortment-inventory optimization problem, suggesting that our algorithm achieves nearly optimal regret rate, provided that the static optimization oracle is exact. Then we incorporate more practical approximate static optimization oracles into our algorithm, and bound from above the impact of static optimization errors on the regret of our algorithm.
主讲人简介
梁湧，清华大学经济管理学院管理科学与工程系副教授，于2005年获得清华大学学士学位，2008年获得普渡大学硕士学位，2013获得加州大学伯克利分校工业工程及运筹学博士学位。2013-2014曾任职于Google公司负责数据中心优化。目前研究兴趣包括数据驱动优化理论；优化理论以及其在运营管理中的应用；动态机制设计等。在 Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, INFORMS Journal on Computing, IISE Transactions 等高质量期刊发表多篇论文。主持了国家自然科学基金青年、面上项目等，并与企业合作进行了一些学术成果的落地。
