A reinforcement learning approach to distribution-free capacity allocation for sea cargo revenue management

Dong Wook Seo, Kyuchang Chang, Taesu Cheong, Jun Geol Baek

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose learning-based adaptive control based on reinforcement learning for the booking policy in sea cargo revenue management. The problem setting is that the demand distribution is unknown while the historical data is available, and the problem is formulated as a stochastic dynamic programming model. We demonstrate the existence of an optimal control limit policy and investigate the important properties and optimal policy structures of the model. We then propose a reinforcement learning approach for the data-driven approximation of the optimal booking policy to maximize shipping line revenue. The performance of the proposed approach is very close to that of the optimal policy and superior to that of the EMSR-b algorithm.

Original languageEnglish
Pages (from-to)623-648
Number of pages26
JournalInformation Sciences
Volume571
DOIs
Publication statusPublished - 2021 Sep

Keywords

  • Liner shipping
  • Reinforcement learning
  • Revenue management
  • Stochastic dynamic programming

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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