报告题目：Refund or Not? Joint Pricing and Refund Optimization for Omnichannel Retailing with Product Returns
报告人简介：严珍珍博士，新加坡南洋理工大学物理与数学科学学院&南洋商学院副教授，博士生导师。她的研究主要聚焦于数据驱动的资源分配问题，开创了一系列鲁棒且响应迅速的方法，在处理中等规模数据集方面尤为有效。其研究成果具有广泛的应用前景，涵盖了智慧城市运营、供应链管理和电子商务运营等多个领域。通过开发先进的模型和分析工具，她致力于解决现实世界中的挑战，特别是在去中心化和动态变化的环境中，确保解决方案兼具可扩展性和实用性。她的研究成果丰硕，已在《Management Science》《Operations Research》《MSOM》和《POMS》等运营管理领域的顶级期刊上发表论文十余篇。出色的研究为她赢得了诸多荣誉，包括入围2020年MSOM数据驱动研究挑战赛、荣获2022年南洋理工大学物理与数学科学学院青年研究员奖以及2023年亚太运筹学会青年研究员最佳论文奖。她的研究成果也常常见诸The Straits Times和ScienceDaily等主流媒体。目前，她担任《Decision Sciences》期刊的副主编，并出任新加坡运筹学会会长。在科研之外，她也是一位杰出的教育工作者，凭借卓越的教学表现荣获了享有盛誉的2024年南洋理工大学教育奖（院级）。
报告摘要：We consider an omnichannel retailer selling multiple substitutable products through an online channel and a physical store. Online purchases can be returned either by mail or to the physical store. The retailer decides each product’s price and refund value for each channel to maximize his expected profit.
We capture a consumer's sequential decisions on making a purchase and potentially returning her product using a generalized Markovian logit choice (MLC) model. We use this model to formulate the retailer's joint pricing and refund optimization problem. If there are constraints on prices and refund values, then this problem may become non-convex, and we approximate it using a mixed-integer linear program (MILP). Furthermore, we analytically derive the performance accuracy of MILP. Otherwise, this problem is convex, and we analytically obtain its optimal solution. We estimate the generalized MLC model using transaction data, making our framework applicable to a data-driven setting. Numerical experiments using synthetic data demonstrate that our estimation-and-optimization framework based on the generalized MLC model fits a general data set well. A case study using data from a major fashion retailer demonstrates that our framework can handle situations with partially observable data and is flexible to incorporate various practical refund policies.
We find that to benefit from return policies, the retailer needs to guarantee good quality or non-negative online (or offline) return social welfare for each product. We also find that products with a smaller loss coefficient result in a higher return rate but a larger profit.
