报告题目 | Assortment Optimization with Downward Feasibility: Efficient Heuristics Based on Independent Demand |
报告人（单位） | 高品(香港中文大学（深圳），数据科学学院) |
主持人（单位） | 陈静（东南大学） |
时间地点 | 2024年8月28日下午16点经管楼B201 |
报告摘要和内容： One of the key challenges in revenue management is to find a subset of products or services to offer customers to maximize the expected revenue of a firm. Often, the set of feasible assortments must satisfy multiple business constraints. In this paper, we develop a general heuristic for the assortment optimization problem that can be applied to various choice models. Our investigation reveals that while optimizing assortment decisions needs to account for substitution effects among products, we advocate simplifying the conundrum to one akin to the independent demand model, where the demand for a product is independent of other products and all product revenues are uniformly reduced by a constant. The heuristic requires minimal information about the underlying choice model, specifically only the last-choice probabilities, which are defined as the purchase probabilities when the assortment consists of only the product by itself. For weakly regular models that satisfy certain bounding conditions, our heuristic achieves the best possible performance guarantees and necessitates solving only two linear programs when the constraints are totally unimodular (TUM) and downward feasible. More generally, we propose heuristic variants for cases where constraints are not TUM or the bounding conditions are challenging to determine. Leveraging the proposed methodology, we also develop nontrivial heuristics for some well-studied models, either by relaxing the assumption or improving the approximation ratio. Computational experiments highlight our heuristic's exceptional performance. | |
Dr. Pin Gao is an assistant Professor at the School of Data Science, the Chinese University of Hong Kong, Shenzhen. He received his B.S. in Physics from Wuhan University and his MPhil in Physics and Ph.D in Operations Research from The Hong Kong University of Science and Technology. Dr. Gao's research interests include revenue management and operations management in emerging business models. His papers have been published in Management Science, Operations Research, Manufacturing & Service Operations Management. He has received multiple research awards such as the ISCOM best paper (first place) and the POMS-China best paper (second place). | |
