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Matching and Learning for Service Platforms

2023-12-19  

报告题目:Matching and Learning for Service Platforms

报告人:孙旭 助理教授 迈阿密赫博塔商学院 迈阿密大学

邀请人:都牧 副教授

时间:20231221日(周四),上午9:30-10:30

地点:正规买球app排行十佳平台 B312

报告摘要:

  We study dynamic matching in a service platform represented by a multi-class, multi-server queueing system. Matching is in the form of job-server assignments, and the service platform lacks knowledge of job-server-specific mean rewards. The objective is to minimize regret, defined as the difference between the cumulative payoff over a time horizon and the maximum payoff possible, with complete knowledge of all system parameters and stochasticity absent. We propose two matching and learning algorithms: one close in spirit to the barrier method in optimization and the other using Lyapunov optimization. Both algorithms estimate job-server assignment rewards using a bandit learning subroutine. When the total service capacity exceeds the demand, both algorithms achieve sub-linear regret and maintain queue stability. Numerical experiments with synthetic and real-world data validate the effectiveness of our algorithms. The study shows that, despite not being explicitly modeled as part of the managerial objective, queueing delays can impede learning, ultimately reducing platform profitability. Therefore, reducing service delays can be crucial not only to achieving high service levels but also to maximizing rewards.

报告人简介:

  Xu Sun is an assistant professor in the Department of Management Science at Miami Herbert Business School, University of Miami. He received his BS in Applied Mathematics from Dalian University of Technology in 2010, his MS in Probability and Statistics from Zhejiang University in 2012, and his PhD in Operations Research from Columbia University in 2019. His research focuses on the modeling, analysis, and control of dynamic systems, with applications spanning call center operations, smart manufacturing, dynamic pricing, and energy systems. Currently, he takes an interest in developing algorithms for online decision-making, drawing upon applied probability and modern optimization techniques. The central theme is to craft algorithms that not only offer strong theoretical guarantees but are also simple, intuitive, and thus practical for real-world implementation. His research findings were published in top academic journals, including Operations Research, Manufacturing and Service Operations Management, Mathematics of Operations Research, and so on. He was a finalist in the Best Student Paper Competition in the INFORMS Finance Section in 2017 and is a recent recipient of the Travel Award from the Institute for Mathematics and its Applications.


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