Applied Genetic Algorithm for Solving Rich VRP

In this article, we present a study of the effectiveness of a genetic algorithm (GA) to solve a combinatorial problem, that is, a vehicle routing problem (VRP). We propose a new selection method, called “rank and select,” based on selection rate, and we compare it with roulette wheel selection. In this article, we use two types of crossover method and two types of mutation method. These are applied for comparing the best fitness at the end of a generation. The problem solved in this study is how to generate feasible route combinations for a rich VRP and meet all the requirements with an optimum solution. Initial test results show that the route produced by the GA was effectively used for solving rich VRP and especially for a large number of customers, depots, and vehicles. Fuel consumption by proposed routes was lower by about 20.38% compared to that of an existing route.