Multi-vehicle clustered traveling purchaser problem using a variable-length genetic algorithm

Abstract

In this paper, we propose a multi-vehicle clustered traveling purchaser problem (MVCluTPP). Here, two types of procurement planning are proposed. In the first setup, the purchaser visits the markets clusterwise and, after satisfying the demand, returns to the depot with the purchased products, which are carried on the same path using a different vehicle. The other set up, in which products are purchased clusterwise and transported directly from the center of the cluster to the depot, but with the mandate that the purchaser visits the markets clusterwise. One of the multi-pronged aims of the model is to select the clusters and identify which markets to visit, determine the amount of procurement available in each market in a cluster, and develop an optimal routing plan in such a way that the overall system cost is minimized. The clusters are generated using k-means algorithm, and a variable-length chromosome genetic algorithm (VLC-GA) is proposed to optimize the cluster paths and to use a local heuristic to link the clusters to minimize the system cost. Furthermore, the superiority of the VLC-GA has been established through standard TPP and TSP instances, compared with exact methods, and some statistical tests are presented.