Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems

Joren Gijsbrechts, Robert N. Boute, Jan A. Van Mieghem, Dennis J. Zhang

Research output: Indexed journal article Articlepeer-review

33 Citations (Scopus)

Abstract

Problem definition: Is deep reinforcement learning (DRL) effective at solving inventory problems? Academic/practical relevance: Given that DRL has successfully been applied in computer games and robotics, supply chain researchers and companies are interested in its potential in inventory management. We provide a rigorous performance evaluation of DRL in three classic and intractable inventory problems: lost sales, dual sourcing, and multi-echelon inventory management. Methodology: We model each inventory problem as a Markov decision process and apply and tune the Asynchronous Advantage Actor-Critic (A3C) DRL algorithm for a variety of parameter settings. Results: We demonstrate that the A3C algorithm can match the performance of the state-of-the-art heuristics and other approximate dynamic programming methods. Although the initial tuning was computationally demanding and time demanding, only small changes to the tuning parameters were needed for the other studied problems. Managerial implications: Our study provides evidence that DRL can effectively solve stationary inventory problems. This is especially promising when problem-dependent heuristics are lacking. Yet, generating structural policy insight or designing specialized policies that are (ideally provably) near optimal remains desirable.
Original languageEnglish
Pages (from-to)1349-1368
Number of pages20
JournalM&som-manufacturing & Service Operations Management
Volume24
Issue number3
Early online dateJan 2022
DOIs
Publication statusPublished - Jun 2022
Externally publishedYes

Keywords

  • OM-information technology interface
  • Inventory theory and control
  • Logistics and transportation
  • Supply chain management

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