A data-driven approach to modeling assortment optimization: The tractable case of similar substitutes

报告题目：A data-driven approach to modeling assortment optimization: The tractable case of similar substitutes

报告人：江波  教授（上海财经大学）

主持人：王祥丰  副教授

报告时间：2024年3月18日（星期一）14:00-15:00

报告地点：华东师范大学普陀校区理科楼B112

报告摘要：

We propose a data-driven approach to model assortment optimization problems based on three real data sets. Our work is motivated by two empirical observations from customers browsing history on Taobao: one is that most customers browse very few items (≤ 5) before they make a purchase; the second is that there exists a sorting of items so that customer consideration sets are a small interval in the sorting. This algorithm sorting can be discovered by an algorithm due to Cuthill and McKee (1969). Based on these empirical observations, we build a framework for choice models, and show the connection between our framework and some popular choice models. To verify that models under our framework capture reality well, we use the dataset from Bodea et al. (2009) to fit different models and compare their performance on out-of-sample data. The result shows that our model provides a good balance between prediction accuracy and model complexity. Then, we consider the assortment optimization and pricing problem under our model and give fixed-parameter tractable algorithms for both problems. Finally, we implement our approach—going from data to modeling, and finally to optimization—on a third data set of customer clicking history on JD.com.

报告人简介：

       江波教授于2013年获得美国明尼苏达大学博士学位，国家高层次青年人才计划入选者，上海市高层次人才，国家自然科学基金重大项目课题负责人。主要研究方向为：运筹优化、收益管理、机器学习等。有10多篇论文发表于运筹优化与机器学习的国际顶级期刊《Operations Research》、《Mathematics of Operations Research》、《Mathematical Programming》、《SIAM Journal on Optimization》、《INFORMS Journal on Computing》、《Journal of Machine Learning Research》。论文引用人包括多位冯·诺依曼理论奖得主，美国三院院士等学术权威。帮助中国著名企业如京东、顺丰、永辉、太平洋保险等解决仓库优化、定价、选址、排班等问题中的核心难题，取得了良好的实践效果。获得了中国运筹学会青年科技奖、上海市自然科学奖二等奖、宝钢优秀教师奖，上海市教学成果一等奖等荣誉。