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 language | English |
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Article number | 103239 |
Journal | Computers in Industry |
Volume | 119 |
DOIs | |
Publication status | Published - Aug 2020 |
Externally published | Yes |
Keywords
- Collaborative shipping
- Deep reinforcement learning
- Joint replenishment problem
- Machine learning
- Physical internet
- Proximal policy optimization