A Comparative Analysis of Genetic Algorithm and LINGO for an Inbound Transportation Model

Supply chain management (SCM) has become a topic of critical importance for both companies and researchers today. Supply chain optimization problems are formulated as linear programing problems with costs of transportation that arise in several real-life applications. While optimizing supply chain problems, inbound logistic segment has been considered as one of the most neglected area in SCM. Very few studies have focused on utilizing optimization model on SCM that only accounts for inbound logistic system. This study has identified the research gap and proposed method attempts to minimize the total transportation costs of inbound logistic system with reference to available resources at the plants, as well as at each depot. Genetic algorithm and Lingo were approached to help the top management in ascertaining how many units of a particular product should be transported from plant to each depot so that the total prevailing demand for the company’s products satisfied, while at the same time the total transportation costs are minimized. Finally, a case study involving a Bangladeshi renowned retail super shop is used to validate the performance of the algorithm. In order to evaluate the performance of the proposed genetic algorithm, the obtained result was compared with the outputs of LINGO 17.0. Computational analysis shows that the GA has result very close to optimal solution in very large-sized problems, and in case of small problems, LINGO that means exact method works better than heuristics