An Optimization Model for Integrated Warehouse-Inventory-Transportation of a Multi-Echelon Supply Chain

Abstract

Efficient supply chain management is crucial for cost reduction and service level optimization in today's competitive business landscape. This study presents a Mixed Integer Linear Programming (MILP) model to integrate warehouse, inventory, and transportation planning in a multi-echelon supply chain. The proposed model minimizes total operational costs while ensuring timely deliveries and maintaining optimal inventory levels. Key decision variables include warehouse capacities, inventory replenishment policies, and transportation routes, while constraints address demand fulfillment, transportation limitations, and storage capabilities. Computational experiments used realistic supply chain data to validate the model's effectiveness, demonstrating significant cost savings and efficiency improvements compared to conventional approaches. The results show that an integrated approach enhances supply chain resilience and sustainability, providing actionable insights for managers and decision-makers. This study contributes to supply chain optimization by offering a novel framework that bridges the gap between siloed decision-making and holistic data-driven strategies for enhanced operational performance.