Use of Proximal Policy Optimization for the Joint Replenishment Problem

Nathalie Vanvuchelen*, Joren Gijsbrechts, Robert Boute

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Deep reinforcement learning has been coined as a promising research avenue to solve sequential decision-making problems, especially if few is known about the optimal policy structure. We apply the proximal policy optimization algorithm to the intractable joint replenishment problem. We demonstrate how the algorithm approaches the optimal policy structure and outperforms two other heuristics. Its deployment in supply chain control towers can orchestrate and facilitate collaborative shipping in the Physical Internet.

Original languageEnglish (US)
Article number103239
JournalComputers in Industry
Volume119
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Collaborative Shipping
  • Deep Reinforcement Learning
  • Joint Replenishment Problem
  • Machine Learning
  • Physical Internet
  • Proximal Policy Optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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