Use of Proximal Policy Optimization for the Joint Replenishment Problem

Nathalie Vanvuchelen, Joren Gijsbrechts, Robert Boute

Producció científica: Article en revista indexadaArticleAvaluat per experts

64 Cites (Scopus)

Resum

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.

Idioma originalAnglès
Número d’article103239
RevistaComputers in Industry
Volum119
DOIs
Estat de la publicacióPublicada - d’ag. 2020
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