Optimization of Transportation Cost Using Least Cost Method and Vogel’s Approximation Method Implemented in Python

Abstract

Transportation problems are a fundamental class of optimization problems in Operations Research that aim to minimize the cost of distributing goods from multiple supply locations to multiple demand locations. Efficient allocation strategies are required to reduce operational costs and improve logistics performance. This study presents the implementation of two classical techniques used to obtain an initial feasible solution to transportation problems: the Least Cost Method (LCM) and Vogel’s Approximation Method (VAM). The algorithms are implemented using Python to automate computations and reduce manual calculation complexity. A comparative analysis is conducted using a dataset consisting of multiple supply and demand nodes. The results show that Vogel’s Approximation Method produces solutions that are closer to the optimal transportation cost compared to the Least Cost Method. The study highlights how computational tools can improve decision-making in logistics and supply chain management.