Multi-Objective Optimization of Supplier and Distribution Center Location Selection with Inventory Allocation in an Uncertain Environment Considering CO2 Emissions

Abstract

This research focuses on efficiently selecting supplier and distribution center locations in a stochastic supply chain environment. It formulates the problem as a multi-objective optimization model aiming to minimize establishment costs, inventory expenses, and transportation costs while considering capacity limitations. To solve this complex problem, the study uses a single-objective mixed integer programming model along with LP-metrics and the T-H method. The research conducts a thorough comparison of two different methods in terms of solution quality and computational efficiency, supported by statistical hypothesis testing. Additionally, multi-criteria decision-making techniques like VIKOR and PROMETHEE II are applied to rank the effectiveness of these methods. The proposed model is validated through thirty sample problems, demonstrating its reliability and suitability for addressing the challenges of supplier and distribution center location selection in an uncertain supply chain environment.