报告题目： Assign-to-Seat: Dynamic Capacity Control for Selling High-Speed Train Tickets
报 告 人： 周明龙 复旦大学
报告时间：2023年10月11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 introduce a novel approach to prescriptive analytics that leverages robust satisficing techniques to determine optimal decisions in situations of risk ambiguity and prediction uncertainty. Our decision model relies on a reward function that incorporates uncertain parameters, which can be partially predicted using available side information. However, the accuracy of the linear prediction model depends on the quality of regression coefficient estimates derived from the available data. To achieve a desired level of fragility, we begin by establishing a target relative to the predict-then-optimize objective and solve a residual-based robust satisficing model. Next, we solve a new estimation-fortified robust satisficing model that minimizes the influence of estimation uncertainty while ensuring that the estimated fragility of the solution in achieving a less ambitious guarding target falls below the level for the desired target. We demonstrate the effectiveness of our approach through case studies involving a wine portfolio investment problem and a multi-product pricing problem using real-world data. Our numerical studies show that our approach outperforms the predict-then-optimize approach in achieving higher expected rewards and at lower risks when evaluated on the actual distribution.
主讲人简介
周明龙现为复旦大学管理学院管理科学系青年副研究员，2021年博士毕业于新加坡国立大学商学院，曾在新加坡国立大学运筹与分析中心进行博士后工作。周明龙的研究兴趣主要包括数据驱动的决策模型、鲁棒优化、预约及调度、机器学习等，在OR、MSOM、POM期刊发表论文四篇。
