American Journal of Industrial Engineering, 2019, Vol. 6, No. 1, 13-18 Available online at http://pubs.sciepub.com/ajie/6/1/2 Published by Science and Education Publishing DOI:10.12691/ajie-6-1-2 A Comparative Analysis of Genetic Algorithm and LINGO for an Inbound Transportation Model Humaira Nafisa Ahmed * , Sayem Ahmed, Md. Nazmus Sakib, M. M. Mahbubur Rahman Department of Mechanical & Production Engineering, Ahsanullah University of Science & Technology, Dhaka-1208, Bangladesh *Corresponding author: humairanafisa380@gmail.com Received August 16, 2019; Revised September 26, 2019; Accepted October 15, 2019 Abstract 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. Keywords: supply chain, genetic algorithm, LINGO, inbound transportation cost Cite This Article: Humaira Nafisa Ahmed, Sayem Ahmed, Md. Nazmus Sakib, and M. M. Mahbubur Rahman, “A Comparative Analysis of Genetic Algorithm and LINGO for an Inbound Transportation Model.” American Journal of Industrial Engineering, vol. 6, no. 1 (2019): 13-18. doi: 10.12691/ajie-6-1-2. 1. Introduction Supply chain management is a field of growing interest for both companies and researchers. Supply chain management (SCM) definition varies from one enterprise to another. This chain is concerned with two distinct flows: a forward flow of materials and backward flow of information. At its highest level, a supply chain is comprised of two basic, integrated process: (1) The Production Planning and Inventory Control Process and (2) The Distribution and Logistic Process. The aim of logistics activities, as a bridge between manufacturers and customers, is to bring the right product to the right place in the right quantity at the right time [1]. Inbound and outbound logistics combine within the field of supply-chain management, as managers seek to maximize the reliability and efficiency of distribution networks while minimizing transport and storage costs. Inbound logistics refers to the transport, storage and delivery of goods coming into a business whereas outbound logistics refers to the same for goods going out of a business. According to Ali Naimi Sadigh [2] supply chain is an interrelating network of suppliers, manufacturers, distributors, and customers, plays an important role in competitive markets to satisfy customer demands. Recently it has been found that product delivery to customers in a suitable time with desirable quality and minimum cost is a complicated process that needs several internal and external organizational transactions. Since efficiency and responsiveness are two generic strategies for supply chain network design, coordination of these transactions is an important issue. The reason behind transportation’s having a prime role in supply chain management is because products are never produced and consumed in the same place. According to the studies of [3], Genetic Algorithms work better where the traditional search and optimization algorithms fail to avail the goal performance. Genetic algorithm is the most popular algorithm that has been used to select optimal route. Many researchers are working on it to optimize routes in supply chain networks using Genetic algorithm. In this research GA was approached to get the total optimized cost and allocation of truckloads and prioritized further. Our proposed model is composed of single objective function to minimize the total transportation costs between plant & depot and as well as to determine the best optimal truck load that to be transported from plant to depot. The remainder of this paper is as follows. In Section 2, literature review of the approached problem is presented. Section 3 presents a descriptive idea about linear programing. The ordinary Genetic Algorithm is introduced in