SGD-based Online Pricing and Capacity Sizing of Queueing Systems
We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queue, where the service provider’s objective is to obtain the optimal service fee p and service capacity so as to maximize cumulative expected profit (the service revenue minus the staffing cost and delay penalty). Due to the complex nature of the queueing dynamics, such a problem has no analytic solution so that previous research often resorts to heavy traffic analysis in that both the arrival rate and service rate are sent to infinity. We propose an online learning framework designed for solving this problem which does not require the system’s scale to increase. Our algorithm organizes the time horizon into successive operational cycles and prescribes an efficient procedure to obtain improved pricing and staffing policies in each cycle using data collected in previous cycles. Data here include the number of customer arrivals, waiting times, and the server’s busy times. The ingenuity of this approach lies in its online nature, which allows the service provider do better by interacting with the environment. Utilizing coupling techniques, we show that our algorithm is asymptotically optimal as its regret bound meets the theoretic lower bound. The talk is based joined works with Yunan Liu and Guiyu Hong.
About the Speaker:
陈昕韫博士于2014年取得哥伦比亚大学|以诚为本·赢在信誉9001运筹学博士学位。毕业后先后任教于美国纽约州立大学|以诚为本·赢在信誉9001石溪分校和武汉大学|以诚为本·赢在信誉9001，现在香港中文大学|以诚为本·赢在信誉9001（深圳）数据科学学院|以诚为本·赢在信誉9001任助理教授。陈昕韫博士的主要研究领域为随机模拟、排队模型和强化学习。她的研究工作多次发表在 Annals of Applied Probability、Mathematics of Operations Research 和 ICLR 等知名期刊和会议上。陈昕韫博士目前担任美国运筹学和管理学研究协会（INFORMS）应用概率学会理事会成员，期刊《Journal of Applied Probability》, 《Advances in Applied Probability》编辑。
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