Reinforcement learning for freight booking control problems

Justin Dumouchelle (), Emma Frejinger () and Andrea Lodi ()
Additional contact information
Justin Dumouchelle: University of Toronto
Emma Frejinger: DIRO (FAS), Université de Montréal
Andrea Lodi: Jacobs Technion-Cornell Institute, Cornell Tech and Technion - IIT

Journal of Revenue and Pricing Management, 2024, vol. 23, issue 4, No 5, 318-345

Abstract: Abstract Booking control focuses on the problem of deciding whether to accept or reject bookings to maximize revenue while considering limited capacity. For freight applications, computing the cost of fulfilling requests requires solving an operational decision-making problem which often corresponds to a mixed-integer linear program. We propose a two-phase learning-based approach that first learns to predict the objective of the operational problem, then leverages the prediction within reinforcement learning algorithms to compute the policies. The method is general and applies to different problems faced in practice. We show strong performance on two booking control problems in the literature: distributional logistics and airline cargo management.

Keywords: Revenue management; Booking control; Reinforcement learning; Supervised learning; Freight transportation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1057/s41272-023-00459-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorapm:v:23:y:2024:i:4:d:10.1057_s41272-023-00459-1

Ordering information: This journal article can be ordered from
https://www.palgrave.com/gp/journal/41272

DOI: 10.1057/s41272-023-00459-1

Access Statistics for this article

Journal of Revenue and Pricing Management is currently edited by Ian Yeoman

More articles in Journal of Revenue and Pricing Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().