Dynamic pricing of regulated field services using reinforcement learning.

Mandania, Rupal; Oliveira, Fernando S.

Abstract

Resource flexibility and dynamic pricing are effective strategies in mitigating uncertainties in production systems; however, they have yet to be explored in relation to the improvement of field operations services. We investigate the value of dynamic pricing and flexible allocation of resources in the field service operations of a regulated monopoly providing two services: installations (paid-for) and maintenance (free). We study the conditions under which the company can improve service quality and the profitability of field services by introducing dynamic pricing for installations and the joint management of the resources allocated to paid-for (with a relatively stationary demand) and free (with seasonal demand) services when there is an interaction between quality constraints (lead time) and the flexibility of resources (overtime workers at extra cost). We formalize this problem as a contextual multi-armed bandit problem to make pricing decisions for the installation services. A bandit algorithm can find the near-optimal policy for joint management of the two services independently of the shape of the unobservable demand function. The results show that (i) dynamic pricing and resource management increase profitability; (ii) regulation of the service window is needed to maintain quality; (iii) under certain conditions, dynamic pricing of installation services can decrease the maintenance lead time; (iv) underestimation of demand is more detrimental to profit contribution than overestimation.

Citation

MANDANIA, R. and OLIVEIRA, F.S. 2023. Dynamic pricing of regulated field services using reinforcement learning. IISE transactions [online], 55(10), pages 1022-1034. Available from: https://doi.org/10.1080/24725854.2022.2151672

• Journal Article Type: Article
• Acceptance Date: Nov 6, 2022
• Online Publication Date: Jan 19, 2023
• Publication Date: Oct 31, 2023
• Deposit Date: Oct 21, 2023
• Publicly Available Date: Oct 31, 2023
• Journal: IISE Transactions
• Print ISSN: 2472-5854
• Electronic ISSN: 2472-5862
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 55
• Issue: 10
• Pages: 1022-1034
• DOI: https://doi.org/10.1080/24725854.2022.2151672
• Keywords: Dynamic pricing; Quality management; Regulation; Reinforcement learning
• Public URL: https://rgu-repository.worktribe.com/output/2078483
• Additional Information: This article has been published with separate supporting information. This supporting information has been incorporated into a single file on this repository and can be found at the end of the file associated with this output.