Optimizing Supplier Selection Under Risk: A Multi-Method Approach

This paper presents an innovative approach to optimize supplier selection while minimizing transportation costs in supply chain management. The methodology integrates Mixed Integer Linear Programming (MILP), Genetic Algorithm (GA), and a stochastic programming approach (MILP combined with Monte Carlo simulation, called MCLP) to address the complexities of supplier selection. The MILP model is designed to incorporate various factors such as supplier reliability, predictable demand, and risk factors, thereby providing a comprehensive evaluation framework. To efficiently solve the MILP model, the GA is utilized to explore the solution space and identify near-optimal solutions. Additionally, MCLP is integrated to account for uncertainty and variability in the supply chain environment, enhancing the robustness of the optimization approach. This research contributes to advancing supply chain optimization by offering a practical framework that considers multiple criteria and minimizes transportation costs in supplier selection processes.