Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 72 references that are in the map. First posted on arXiv 26 Jul 2025.
Builds on
10 of 72 references.
References · 10
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Citing papers per year: 2026: 2.
2 papers.
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- 2025Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management
- 2025Benchmarking MOEAs for solving continuous multi-objective RL problems
- 2026Multi-Objective Reinforcement Learning for Large-Scale Tote Allocation in Human-Robot Collaborative Fulfillment Centers
- 2025Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
- 2025Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions
- 2024Generative Probabilistic Planning for Optimizing Supply Chain Networks