Optimization of Leather Supply Chain Logistics Using Reinforcement Learning and Multi-Agent Simulation Models
Keywords:
Leather supply chain, logistics optimization, reinforcement learning, multi-agent systems, dynamic scheduling, resource allocation, production planning, MAS, Q-learning, policy optimizationAbstract
In the leather industry, supply chain logistics are characterized by complex, multi-stage networks involving raw material procurement, processing, transportation, and finished goods distribution. Inefficiencies across these stages often result in delays, quality degradation, and cost overruns. This paper proposes a hybrid optimization framework that integrates reinforcement learning (RL) and multi-agent simulation (MAS) to enhance decision-making across the leather supply chain. By simulating agent-based interactions and dynamically adjusting policies through RL, the system adapts to real-time variables like demand shifts, lead times, and production constraints. Results demonstrate a reduction in bottlenecks, enhanced delivery accuracy, and improved cost-efficiency over traditional linear planning models.
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Copyright (c) 2020 Guruprasath Raja L (Author)
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