K-means clustering and genetic algorithm to solve vehicle routing problem with time windows problem

Abstract

Distribution is an important aspect of industrial activity to serve customers on time with minimal operational cost. Therefore, it is necessary to design a quick and accurate distribution route. One of them can be design travel distribution route using k-means method and genetic algorithms. This research will combine k-means method and genetic algorithm to solve vehicle routing problem with time windows (VRPTW). K-means can do clustering properly and genetic algorithms can optimize the route. The proposed genetic algorithm employs initialize chromosome from the result of k-means and using replacement method of selection. Based on the comparison between genetic algorithm and hybrid k-means genetic algorithm proves that k-means genetic algorithm is a suitable combination method with relative low computation time, are comparison between 2700 and 3900 seconds.