Dynamic pricing policies for interdependent perishable products or services using reinforcement learning.

Rana, Rupal; Oliveira, Fernando S.

Abstract

Many businesses offer multiple products or services that are interdependent, in which the demand for one is often affected by the prices of others. This article considers a revenue management problem of multiple interdependent products, in which dynamically adjusted over a finite sales horizon to maximize expected revenue, given an initial inventory for each product. The main contribution of this article is to use reinforcement learning to model the optimal pricing of perishable interdependent products when demand is stochastic and its functional form unknown. We show that reinforcement learning can be used to price interdependent products. Moreover, we analyze the performance of the Q-learning with eligibility traces algorithm under different conditions. We illustrate our analysis with the pricing of services.

Citation

RANA, R. and OLIVEIRA, F.S. 2015. Dynamic pricing policies for interdependent perishable products or services using reinforcement learning. Expert systems with applications [online], 42(1), pages 426-436. Available from: https://doi.org/10.1016/j.eswa.2014.07.007

• Journal Article Type: Article
• Acceptance Date: Jul 26, 2014
• Online Publication Date: Jul 26, 2014
• Publication Date: Jan 31, 2015
• Deposit Date: Oct 21, 2023
• Publicly Available Date: Nov 15, 2023
• Journal: Expert systems with applications
• Print ISSN: 0957-4174
• Electronic ISSN: 1873-6793
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 1
• Pages: 426-436
• DOI: https://doi.org/10.1016/j.eswa.2014.07.007
• Keywords: Dynamic pricing; Revenue management; Service management; Multi-criteria decision making; Profit simulation
• Public URL: https://rgu-repository.worktribe.com/output/2114744