Optimization of Supplier Selection and Order Allocation Considering Supply Risk based on MRFO Algorithm

Abstract

This study develops a multi-period procurement model integrating Mean-CVaR optimization to address supply-demand uncertainty in automotive supply chains. We propose a modified Manta Ray Foraging Optimization algorithm that minimizes total costs while controlling quality and delivery risks under stochastic conditions. Using real manufacturing data, the model demonstrates 39.6% cost reduction versus conventional methods while maintaining 95% service levels. Key findings identify optimal risk parameters (λ=0.4) and prove multi-supplier strategies (≥3 partners) enhance resilience when α≥0.9. The framework provides actionable insights for sustainable procurement planning.