Integrating MILP and MCDM for Smart Warehouse Location Selection in Agricultural E-Commerce

Abstract

Optimizing warehouse locations is critical for reducing logistics costs, improving delivery reliability, and enhancing sustainability in agricultural e-commerce. This study applies Mixed-Integer Linear Programming (MILP) to filter optimal warehouse alternatives, followed by MARCOS for ranking. The findings indicate that MILP significantly impacts the selection process, with G7, G1, and G6 emerging as the top choices. Sensitivity analysis and comparisons with other MCDM methods confirm the robustness of the results. This research offers practical insights for improving supply chain efficiency and contributes to decision-making models for warehouse location optimization.