Using particle swarm optimization with a policy optimization approach to stabilize the supply chain

In this paper, we propose and demonstrate a new methodology to stabilize systems with complex dynamics like the supply chain. This method is based on the Accumulated Deviations from Equilibrium (ADE). It is most beneficial for controlling system dynamic models characterized by multiple types of delays, many interacting variables, and feedback processes. We employ Particle Swarm Optimization as the optimization approach due to its performance in multi-dimensional space, stochastic properties, and global reach. We demonstrate the effectiveness of our method using a manufacturing-supply-chain case study.