# An online learning approach to dynamic pricing and capacity sizing in service systems

arXiv: https://arxiv.org/abs/2009.02911 · HTML: https://mlsanity.com/paper/2009.02911/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Learning-based Optimal Admission Control in a Single Server Queuing System](https://mlsanity.com/paper/2212.11316/) (2022)
- [Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution](https://mlsanity.com/paper/2303.03399/) (2023)
- [Convergence Analysis of Decentralized ASGD](https://mlsanity.com/paper/2309.03754/) (2023)
- [Learning to Admit Optimally in an M/M/k/k+N Queueing System with Unknown Service Rate](https://mlsanity.com/paper/2202.02419/) (2022)
- [Learning-Based Pricing and Matching for Two-Sided Queues](https://mlsanity.com/paper/2403.11093/) (2024)
- [Selecting the Best Optimizing System](https://mlsanity.com/paper/2201.03065/) (2022)
- [Online Non-Stationary Stochastic Quasar-Convex Optimization](https://mlsanity.com/paper/2407.03601/) (2024)

## Abstract (arXiv)

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 μ so as to maximize the 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 where both the arrival rate and service rate are sent to infinity. In this work we propose an online learning framework designed for solving this problem which 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 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 GI/GI/1 queues.