Comparative Analysis of Optimization Models for Transportation Problems

This research paper presents a comprehensive comparative analysis of three optimization models—Simplex Method, Vogel's Approximation Method (VAM), and Genetic Algorithms (GA)-used to solve transportation problems. The study applies these methods to a primary dataset and a more complex dataset, evaluating their performance based on total transportation cost, computational time, and scalability. The results indicate that while the Simplex Method is the most cost-effective, Genetic Algorithms offer superior scalability and flexibility, particularly in handling complex transportation scenarios. The study also discusses the potential for hybrid models to combine the strengths of these methods, providing practical insights for logistics management.