Genetic Algorithm with Cluster-first Route-second to Solve the Capacitated Vehicle Routing Problem with Time Windows

Abstract

In a distribution problem, designing the right distribution route can minimize the total transportation costs. Therefore, this research aims to design a distribution route that produces a minimal distribution distance by clustering the demand points first. We generated the clustering method to cluster the demand points by considering the proximity among the demand points and the total vehicle capacity. In solving this problem, we are using p-median to determine the cluster and a genetic algorithm to determine the distribution route with the characteristics of the CVRPTW problem. CVRPTW or capacitated vehicle routing problem with time windows is a type of VRP problem where there is a limitation of the vehicle capacity and service time range of its demand point. This research concludes that clustering the demand points provides a better result in terms of total distribution costs by up to 16.26% compared to the existing delivery schedule. The performance of the genetic algorithm shows an average difference of 1.73%, compared to the exact or optimal method. The genetic algorithm is 89.68% faster than the exact method in the computational time.