Stochastic Optimization of Supply Chain using Multi-Agent Optimization Framework

Uncertainty in supply chain is one of the biggest problems in real world supply chain management. In this work, we propose an efficient homogeneous multi-agent optimization framework based on efficient simulated annealing where uncertainties are handled using novel efficient sampling technique. We present the results of single, two-agent, and three –agent framework. The work discusses various sampling techniques and the reason for using LHS-SOBOL sampling technique for this work. The promising results of this new framework shows that multi- agent optimization framework can be used for large scale supply chain management problems under uncertainty.