Online reinforcement learning-based inventory control for intelligent E-Fulfilment dealing with nonstationary demand

Daniel Y. Mo, Y. P. Tsang, Y. Wang, Weikun Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In this study, an online reinforcement learning-based approach and a reinforcement learning with prior knowledge approach are proposed to enhance decision intelligence in inventory management systems for handling nonstationary stochastic market demands in e-commerce environment with crowdsourcing resources. The proposed inventory control policies are designed to solve a multi-period inventory problem with the objectives of optimising inventory-related costs and service levels in the absence of prior information on demand patterns. An experimental analysis reveals that the proposed reinforcement learning-based inventory control policies achieve cost savings and higher service levels across various settings of cost ratios and lead times.

Original languageEnglish
Article number2284427
JournalEnterprise Information Systems
Volume18
Issue number2
DOIs
Publication statusPublished - 2024

Keywords

  • inventory policy
  • nonstationary demand
  • online optimal control
  • Reinforcement learning
  • supply chain

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management

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