mlsanityData to 14 Oct 2026

MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization

arXiv 2509.06490 ↗First posted 8 Sep 2025cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

In supply chain management, decision-making often involves balancing multiple conflicting objectives, such as cost reduction, service level improvement, and environmental sustainability. Traditional multi-objective optimization methods, such as linear programming and evolutionary algorithms, struggle to adapt in real-time to the dynamic nature of supply chains. In this paper, we propose an approach that combines Reinforcement Learning (RL) and Multi-Objective Evolutionary Algorithms (MOEAs) to address these challenges for dynamic multi-objective optimization under uncertainty. Our method leverages MOEAs to search the parameter space of policy neural networks, generating a Pareto front of policies. This provides decision-makers with a diverse population of policies that can be dynamically switched based on the current system objectives, ensuring flexibility and adaptability in real-time decision-making. We also introduce Conditional Value-at-Risk (CVaR) to incorporate risk-sensitive decision-making, enhancing resilience in uncertain environments. We demonstrate the effectiveness of our approach through case studies, showcasing its ability to respond to supply chain dynamics and outperforming state-of-the-art methods in an inventory management case study. The proposed strategy not only improves decision-making efficiency but also offers a more robust framework for managing uncertainty and optimizing performance in supply chains.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 51 references that are in the map. First posted on arXiv 8 Sep 2025.

Builds on

7 of 51 references.

References · 7

  1. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  2. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  3. 2018Dynamic Weights in Multi-Objective Deep Reinforcement Learningcited by 100
  4. 2022When Is Partially Observable Reinforcement Learning Not Scary?cited by 100
  5. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  6. 2023An Analysis of Multi-Agent Reinforcement Learning for Decentralized Inventory Control Systemscited by 11
  7. 2024Leveraging Graph Neural Networks and Multi-Agent Reinforcement Learning for Inventory Control in Supply Chainscited by 10

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  1. 2025Reinforcement Learning for Multi-Objective Multi-Echelon Supply Chain Optimisation
  2. 2026Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation
  3. 2026MIRACL: A Diverse Meta-Reinforcement Learning for Multi-Objective Multi-Echelon Combinatorial Supply Chain Optimisation
  4. 2025Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management
  5. 2025Iterative Multi-Agent Reinforcement Learning: A Novel Approach Toward Real-World Multi-Echelon Inventory Optimization
  6. 2025Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness
  7. 2025Classical and Deep Reinforcement Learning Inventory Control Policies for Pharmaceutical Supply Chains with Perishability and Non-Stationarity
  8. 2024Generative Probabilistic Planning for Optimizing Supply Chain Networks