MORSE: An Adaptive Decision-Making Framework Combining Reinforcement Learning and Multi-Objective Evolutionary Algorithms for Dynamic Inventory Control

In supply chain management, decision-making often involves balancing multiple conflicting objectives, such as cost reduction, service level improvement, and environmental sustainability. Traditional methods for multi-objective optimization, such as linear programming and evolutionary algorithms, have proven useful but struggle to adapt in real-time or handle the dynamic nature of supply chains. In this paper, we propose a novel approach that combines Reinforcement Learning (RL) and Multi-Objective Evolutionary Algorithms (MOEAs) to address these challenges. Our method leverages MOEAs to search the parameter space of policy neural networks, resulting in a Pareto front of policies. This equips the decision-maker with a swarm of policies that can be dynamically switched based on the current system conditions and objectives, ensuring flexibility and adaptability in real-time decision-making. We demonstrate the effectiveness of this hybrid approach through a series of case studies that showcase its ability to respond to the changing dynamics of supply chain environments. We also outperform state-of-the-art methods when benchmarking against our inventory management case study. The proposed strategy not only improves decision-making efficiency but also provides a more resilient framework for fast decision-making and handling uncertainty in supply chains.