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

• URL: http://arxiv.org/abs/2509.06490v1
• Date: Mon, 08 Sep 2025 09:51:24 GMT
• Title: MORSE: Multi-Objective Reinforcement Learning via Strategy Evolution for Supply Chain Optimization
• Authors: Niki Kotecha, Ehecatl Antonio del Rio Chanona,
• Abstract summary: In supply chain management, decision-making often involves balancing multiple objectives, such as cost reduction, service level improvement, and environmental sustainability.<n>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.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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