Online Network Revenue Management Using Thompson Sampling

Operations Research, Forthcoming

52 Pages Posted: 3 Apr 2015 Last revised: 5 Mar 2018

See all articles by Kris Ferreira

Kris Ferreira

Harvard Business School

David Simchi-Levi

Massachusetts Institute of Technology (MIT) - School of Engineering

He Wang

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE)

Date Written: November 7, 2017

Abstract

We consider a price-based network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As common in practice, we assume the demand function contains unknown parameters, which must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a tradeoff commonly referred to as the exploration-exploitation tradeoff. Towards the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price ("exploration" objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season ("exploitation" objective). We propose a class of dynamic pricing algorithms that builds upon the simple yet powerful machine learning technique known as Thompson sampling to address the challenge of balancing the exploration-exploitation tradeoff under the presence of inventory constraints. Our algorithms prove to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multi-armed bandit problems with resource constraints, with applications in other revenue management settings and beyond.

Keywords: revenue management, pricing, multi-armed bandit, Thompson sampling

Suggested Citation

Ferreira, Kris and Simchi-Levi, David and Wang, He, Online Network Revenue Management Using Thompson Sampling (November 7, 2017). Operations Research, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2588730 or http://dx.doi.org/10.2139/ssrn.2588730

Kris Ferreira

Harvard Business School ( email )

Boston, MA 02163
United States
617-495-3316 (Phone)

HOME PAGE: http://www.hbs.edu/faculty/Pages/profile.aspx?facId=773347

David Simchi-Levi (Contact Author)

Massachusetts Institute of Technology (MIT) - School of Engineering ( email )

MA
United States

He Wang

Georgia Institute of Technology - The H. Milton Stewart School of Industrial & Systems Engineering (ISyE) ( email )

765 Ferst Drive
Atlanta, GA 30332-0205
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
1,721
Abstract Views
5,170
rank
10,389
PlumX Metrics