Hybrid Optimization Approach for Supply Chain Planning

Abstract

The goal of supply chain planning is designing an optimal and feasible production and distribution plan for the whole supply chain. Two common methods of optimization are analytical and simulation-based optimization. In this paper, both methods are combined to consolidate the strengths of each, also known as the hybrid analytical and simulation approach. A case study of a multi-period, multi-echelon, and multi-product production and distribution problem that maximizes the whole supply chain's profit is introduced, to demonstrate the effectiveness of the proposed hybrid approach. The analytical model is solved to find the ideal optimal production-distribution plan, and then the plan is inputted into a simulation model, where uncertainties are included. The proposed approach then identifies a feasible plan that meets makespan and service level requirements. Safety stock is incorporated to fulfill the service level requirements and maximize the supply chain's profit. This procedure continues iteratively until the production-distribution plan is feasible and optimized. The results show that the proposed approach can solve for an optimal and feasible solution with relatively fast computational time.