A genetic algorithm approach for a real life heterogeneous capacitated vehicle routing problem

The purpose of this study is to understand the supply chain and logistics management generally and then practise a real life application with the most important problem of this discipline. After giving a brief information about supply chain and logistics management, application areas and core problems are introduced. As the case study, the daily distribution planning problem of an automotive company is tackled and designing an efficient solution algorithm for the decision maker is aimed. At the beginning, the definition of real problem is made and all the constraints are put forth for consideration. By examining the existing system and distribution planning process, we realized that we encountered with heterogeneous capacitated vehicle routing problem. First, the mathematical model of the problem was formulated as mixed-integer programming. Then, another literature survey was done for selecting an efficient solution algorithm or heuristic which can give optimum results. Next, genetic algorithm was decided to deal with and the research was focused on related papers. A specific genetic algorithm was developed for solving the problem and programmed in MatLab language. The experimental results showed that the proposed algorithm performs well and produces high-quality solutions which also satisfy the performance target of the company by consuming shorter run-time. The proposed genetic algorithm provides decision maker the opportunity of evaluating alternative distribution plans, as well as saving cost and time.