Minimizing Transportation Cost in Supply Chain Logistics Problem Using Optimization Techniques

Abstract

Supply chain logistics transportation involves planning, coordination and movement of products between warehouses to customers via distribution networks while managing freight costs, warehouse handling costs and operational limitations in order to deliver products in timely and cost-effective manner. In recent years, the COVID-19 pandemic has exerted a significant impact on global supply chains in terms of increased transportation costs and more complicated operational limitations, thereby rendering the minimization of costs a major concern of logistics decision-making. This study deals with the optimization problem of a supply chain logistics data which includes 8,361 customer orders, warehouses, freight costs, warehouse costs and product-warehouse eligibility limits. The main objective of the study is to reduce the total transportation cost while satisfying operational and business restrictions. In order to reach this goal, three optimization methods are applied which are the Vogel's Approximation Method followed by Modified Distribution Method (VAM-MODI), Integer Linear Programming (ILP) and Pre-emptive Goal Programming (GP). The findings explain that both the ILP and Pre-emptive GP models are able to allocate all orders and achieve a total transportation cost reduction from $13,341,913.03 to $13,172,055.04 which indicates a total of 1.27% reduction in the cost in comparison to historical allocations. The Pre-emptive GP model also enhances good delivery performance while retaining the optimum level of cost. In contrast, VAM-MODI only give minimal cost reduction and can only be deployed in a portion of the orders because of the structural and feasibility constraints. The results show that precise and multi-objective optimization techniques give better and more realistic operationally accurate solutions to complex allocation of supply chain logistics problems. Future research may extend this study by incorporating uncertainty, sustainability-related objectives and dynamic demand conditions into the optimization framework.