An Online Learning Approach to Dynamic Pricing and Capacity Sizing in Service Systems

We study a dynamic pricing and capacity sizing problem in a G I / G I / 1 queue, in which the service provider’s objective is to obtain the optimal service fee p and service capacity μ so as to maximize the cumulative expected profit (the service revenue minus the staffing cost and delay penalty). Because of 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 which both the arrival and service rates are sent to infinity. In this work, we propose an online learning framework designed for solving this problem that does not require the system’s scale to increase. Our framework is dubbed gradient-based online learning in queue (GOLiQ). GOLiQ 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 to do better by interacting with the environment. Effectiveness of GOLiQ is substantiated by (i) theoretical results, including the algorithm convergence and regret analysis (with a logarithmic regret bound), and (ii) engineering confirmation via simulation experiments of a variety of representative G I / G I / 1 queues.