Developing a Genetic Algorithm Model for Optimizing Item Delivery and Cost

Vehicle Routing Problem (VRP) is an important problem in logistics and transportation sectors and can be in different forms including optimizing item delivery and cost problems. This paper presents a genetic algorithm model for solving real-world item delivery variant VRP involving multi warehouses/distribution centers and multi clients/demand centers with time windows. Unlike traditional methods that address delivery routing and terminal selection, this study focuses on the vehicle assignment to reduce the transportation cost by clustering the items before assigning them to the appropriate truck to be delivered to the client using genetic algorithm. By applying the manual trial and error parameter calibration method, performance and robustness of the developed model is tested and it identifies an optimal solution, thus minimized transportation cost associated with assigned truck type and the traveling distance.