報告人：北卡羅來納州立大學 Dr . Yunan Liu
時間：2024年4月15日11：00-12：00
地點：彩神v西土城校區教四-441
報告摘要：
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.
專家簡介：
劉雨楠，美國北卡羅來納州立大學工業與係統工程係副教授。於清華大學電氣工程係獲得學士學位，於哥倫比亞大學工業工程與運籌部獲碩士和博士學位。研究興趣包括隨機模型、應用概率、仿真、排隊論、最優控製和強化學習等，並將其應用於客戶聯絡中心（customer contact centers）、醫療保健（healthcare）、生產和運輸係統中。文章發表本領域旗艦期刊上，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。榮獲亞馬遜學者（Amazon Scholar），與亞馬遜客戶服務部的全球容量規劃團隊密切合作。個人網頁：http://yunanliu.wordpress.ncsu.edu
