Optimization of the multi-objective and multi-location transshipment problems with genetic algorithm

Genetic algorithms became very popular on solving logictics and transportation problems. Genetic algorithms give very successful results, especially when dealing with multi-objective optimization problems. In this study, it is aimed to optimize cost, service level, lead times, and quality deficiency objectives of a multi-objective and multi-location transshipment problem at the same time. Preceding studies about transshipment problems deliberated mostly on cost and service level. Furthermore, lead times were added in recent studies. However, quality, which became the most important criterion for producers, retailers, and customers, was mostly disregarded. Therefore it became an obligation for us to integrate the quality objective to our problem. In this way, a more comprehensive and realistic optimization problem was achieved. We propose a genetic algorithm based on the ?Strength Pareto Evolutionary Algorithm? (SPEA2) method to solve this problem. This gives us the opportunity to choose one from the pareto optimal solutions. This approach gives a more realistic and flexible solution to multi-objective optimization problems. In different circumstances, the most convenient solution can be choosen. It is more advantageous to make a decision from a set of best solutions than methods which produce only one solution. Because in this way, the objectives which seem more important for decision makers according to some conditions will be satisfied maximal, at the same time the less important ones will not be disregarded. Keywords: Transshipment problem, multi-objective optimization, genetic algorithm, strength pareto evolutionary algorithm, SPEA2, logistics, supply chain.