Data-driven Distributionally Robust Multiproduct Pricing Problems under Pure Characteristics Demand Models
报告学者:孙海琳
报告者单位:南京师范大学
报告时间:2024年6月21日星期五 15:30-16:15
报告地点:思西501 (腾讯会议号: 600 806 445 线上同步)
报告摘要:This paper considers a multiproduct pricing problem under pure characteristics demand models when the probability distribution of the random parameter in the problem is uncertain. We formulate this problem as a distributionally robust optimization (DRO) problem based on a constructive approach to estimating pure characteristics demand models with pricing by Pang, Su and Lee. In this model, the consumers' purchase decision is to maximize their utility. We show that the DRO problem is well-defined, and the objective function is upper semicontinuous by using an equivalent hierarchical form. We also use the data-driven approach to analyze the DRO problem when the ambiguity set, i.e., a set of probability distributions that contains some exact information of the underlying probability distribution, is given by a general moment-based case. We give convergence results as the data size tends to infinity and analyze the quantitative statistical robustness in view of the possible contamination of driven data. Furthermore, we use the Lagrange duality to reformulate the DRO problem as a mathematical program with complementarity constraints, and give a numerical procedure for finding a global solution of the DRO problem under certain specific settings. Finally, we report numerical results that validate the effectiveness and scalability of our approach for the distributionally robust multiproduct pricing problem.
报告学者简介:孙海琳,南京师范大学数学科学学院教授、博士生导师。2007年在吉林大学获得统计学学士学位,2013年在哈尔滨工业大学获数学博士学位。博士期间在英国南安普顿大学和香港理工大学联合培养。2015-2017年在香港理工大学应用数学系做博士后研究。2018年获中国运筹学会青年科技奖和江苏省数学成就奖,主持国家自然科学基金优秀青年科学基金项目、面上项目和青年科学基金项目。研究领域包括随机优化,分布鲁棒优化、随机变分不等式及其在投资组合、风险管理和经济学模型上的应用。在Mathematical Programming、SIAM Journal on Optimization、Mathematics of Operations Research等国际权威期刊发表了二十多篇论文。