报告人：北卡罗来纳州立大学 Dr . Yunan Liu
时间：2024年4月15日11：00-12：00
地点：北京邮电大学西土城校区教四-441
报告摘要：
In this talk, we investigate new ways to apply machine learning methodologies to queueing models with applications to service systems (e.g., call centers and healthcare). Our work will cover three different machine learning paradigms: (i) online learning, (ii) offline learning, and (iii) deep learning. (i) We propose a new online reinforcement learning technique to solve a multi-period pricing and staffing problem in a service queueing system with an unknown demand curve. We develop an algorithm called gradient-based online-learning in queues (GOLiQ) to dynamically adjust the service price p (and service rate µ) so as to maximize cumulative expected revenues (the sales revenue minus the delay penalty) over a given finite time horizon. (ii) We develop a new simulation-based offline learning algorithm that can be used to determine the required staffing function that achieves time-stable performance for a time-varying queue within a finite time. Our new algorithm, called simulation-based offline learning staffing algorithm (SOLSA), organizes the overall learning process into successive cycles each of which consists of two phases: (1) (Exploitation) The decision maker generates relevant queueing data via a decision-aware simulator under a candidate solution, (2) (Exploration) Using the newly collected data, improved staffing plans are prescribed and to be used to configure the simulator in the next cycle. (iii) We develop a new deep learning method, dubbed deep learning in non-Markovian queues (DeepLiNQ), which is an offline supervised learning method that learns the system’s intrinsic characteristics using synthetic training data. In real-time applications, DeepLiNQ is built by a set of neuro networks and can be used to recursively provide estimates for the transient system waiting time performance.
专家简介：
刘雨楠，美国北卡罗来纳州立大学工业与系统工程系副教授。于清华大学电气工程系获得学士学位，于哥伦比亚大学工业工程与运筹部获硕士和博士学位。研究兴趣包括随机模型、应用概率、仿真、排队论、最优控制和强化学习等，并将其应用于客户联络中心（customer contact centers）、医疗保健（healthcare）、生产和运输系统中。文章发表本领域旗舰期刊上，如Operations Research, Production and Operations Management, INFORMS Journal on Computing, IISE Transactions, Naval Research Logistics, Stochastic Systems, European Journal of Operational Research, Queueing Systems。荣获亚马逊学者（Amazon Scholar），与亚马逊客户服务部的全球容量规划团队密切合作。个人网页：http://yunanliu.wordpress.ncsu.edu
