Multi-agent reinforcement learning-based resilience reconfiguration approach of supply chain system-of-systems under disruption risks

The rapid globalization of supply chains (SC) has opened vast prospects but also increased disruption risks and substantial uncertainties in supply chain systems-of-systems (SCSoSs). Supply chain reconfiguration (SCR) has emerged as a pivotal strategy for mitigating these risks. This paper proposes a multi-agent reinforcement learning-based resilience reconfiguration approach for SCSoSs to address the agile, stable, and spatio-temporal requirements of SCR under disruption risks. It begins by detailing the SCR issue involving suppliers, manufacturers, distributors, and consumers amid disruption risks and introduces three resilience strategies: filling, repairing, and recruiting. A three-phase model for calculating resilience and reconfiguration costs is then developed, grounded in the supply chain directed network (SCDN). Following this, the reconfiguration process is modeled as a partially observable Markov decision process (POMDP), with the state space representing SC elements and the action space including available strategies. The reward function balances resilience and costs considerations. Utilizing the multi-agent proximal policy optimization (MAPPO) technique, the method enables dynamic reconfiguration of SCSoSs, demonstrating its effectiveness through experimental simulations. The analysis also explores how different attributes affect reconfiguration outcomes. Results indicate that the MAPPO approach substantially enhances reconfiguration performance under disruption risks compared to other baselines, providing valuable insights for modern SC management.