主讲人

（单位）

章佳杰

（新加坡国立大学）

主持人

（单位）

何勇

（东南大学）

讲座时间

2025年12 月26日（周五）

下午14点

讲座地点

经管楼B-201

主讲人简介

章佳杰博士本科毕业于东南大学交通学院，硕士毕业于南洋理工大学土木与环境学院，博士毕业于新加坡国立大学工业系统工程与管理系。目前，章博士在新加坡国立大学运筹与分析中心从事博士后研究，合作导师为Prof.Chung PiawTeo。章博士的研究以用户选择建模为核心，聚焦于交通和物流中的规划与管理问题，例如设施选址，选品规划，产品线设计，基于特征的需求预测与定价等等。

讲座内容摘要

Problem Definition: This paper investigates a revenue-maximizing product line design (PLD)problem under the first-choice model, where the utilities of a sample of customers are available and each individual customer selects the option that offers the highest utility.Methodology/results:Recognizing the uncertainty inherent in sampled customer utilities, we propose a distributionally robust optimization (DRO) framework that maximizes the worst-case expected revenue over all utility distributions within a Wasserstein ball centered at the empirical distribution, with the ball radius capturing the degree of robustness. Moreover, to support the practical applicability of our modeling framework, we develop several computational enhancements, including a strengthened reformulation, a parametric approximate method, and a tailored Benders decomposition algorithm. We validate the computational efficiency of these approaches through comprehensive numerical studies.

Managerial Implication: Our analytical results reveal that the seller’s revenue and buyers’ welfare, defined as the mean utility of the selected products over the population, can be aligned in robust product line design. A higher degree of robustness in the DRO model leads to product lines that deliver larger buyers’ welfare. This finding provides a strong economic motivation for revenue-maximizing decision makers to design product lines that are more beneficial to customers. Using a real-world case study of a smart vending machine, we numerically validate that such robust product lines consistently outperform classical designs in both out-of-sample revenue and welfare. These results yield a critical managerial insight: prioritizing buyers’ welfare in PLD not only enhances social value but also strengthens robustness against preference uncertainty, ultimately benefiting both sellers and buyers.