Optimizing Transportation Costs in Supply Chains Using Mixed Integer Linear Programming: A Comprehensive Approach for Logistics Efficiency

Efficient transportation is a critical factor in modern supply chains, influencing both cost management and timely product delivery. This study presents a novel approach for optimizing transportation costs through a Mixed Integer Linear Programming (MIP) model, designed to minimize the overall expense of moving goods from multiple supply nodes (warehouses) to various demand nodes (project sites). The model integrates essential supply chain factors such as freight rates, warehouse costs, and transportation capacity constraints, enabling a holistic optimization strategy. Data preprocessing techniques, including the removal of duplicates, handling of missing values, and standardization of column names, ensure the integrity and consistency of the input data. Exploratory data analysis (EDA) is employed to understand key relationships between shipping costs, service levels, and capacity distributions, which further informs the optimization model. By using MIP, the model selects the most cost-effective transportation routes while adhering to both supply and demand constraints. This study contributes to the development of a robust, data-driven decision-making tool for improving transportation logistics, offering substantial cost savings and enhanced operational efficiency for organizations. The proposed method provides an actionable framework for supply chain managers seeking to optimize logistics networks in an increasingly competitive global marketplace.