Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation

• URL: http://arxiv.org/abs/2507.19788v1
• Date: Sat, 26 Jul 2025 04:30:11 GMT
• Title: Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation
• Authors: Rifny Rachman, Josh Tingey, Richard Allmendinger, Pradyumn Shukla, Wei Pan,
• Abstract summary: The model is evaluated using a multi-objective reinforcement learning (RL) method, benchmarked against an originally single-objective RL algorithm modified with weighted sum.<n>We conduct experiments on varying network complexities, mimicking typical real-world challenges using a customisable simulator.<n>The model determines production and delivery quantities across supply chain routes to achieve near-optimal trade-offs between competing objectives.
• Score: 3.1194372040101928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study develops a generalised multi-objective, multi-echelon supply chain optimisation model with non-stationary markets based on a Markov decision process, incorporating economic, environmental, and social considerations. The model is evaluated using a multi-objective reinforcement learning (RL) method, benchmarked against an originally single-objective RL algorithm modified with weighted sum using predefined weights, and a multi-objective evolutionary algorithm (MOEA)-based approach. We conduct experiments on varying network complexities, mimicking typical real-world challenges using a customisable simulator. The model determines production and delivery quantities across supply chain routes to achieve near-optimal trade-offs between competing objectives, approximating Pareto front sets. The results demonstrate that the primary approach provides the most balanced trade-off between optimality, diversity, and density, further enhanced with a shared experience buffer that allows knowledge transfer among policies. In complex settings, it achieves up to 75\% higher hypervolume than the MOEA-based method and generates solutions that are approximately eleven times denser, signifying better robustness, than those produced by the modified single-objective RL method. Moreover, it ensures stable production and inventory levels while minimising demand loss.

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