An Intelligent K-Means Algorithm for Location-Allocation and Vehicle Routing Problem

Capacitated location-allocation routing problem is about delivering the goods from depot centers to customers. The aims of this problem is to determine the optimum number of depots at the strategic decision level to place factories and allocate customers to these depots, while establishing tour of the vehicle between depots and customers must be built at the tactical or operational levels to supply customers. In recent years several researchers have focused on Location Routing Problem (LRP). We consider in this paper location-allocation routing problem. Our objective is to minimize the routing and location-allocation costs which are main contribution in supply chain costs. Location and routing decisions are interdependent and studies have shown that the overall system cost may be excessive if they are tackled separately. These problems are NP-hard and the combination of them is NP-hard too and solving this problem in medium and large size problem is difficult. In this study, we want to use the business intelligence methods for simplifying the LRP and to solve this problem in large size with exact algorithm. We propose an Intelligent K-means Algorithm (IKMS) for clustering the customer nodes. Then we locate the depots with other k-means algorithm. Finally we allocate these customers to depots. After these steps, we solve the Traveling Salesman Problem (TSP) for each cluster independently, and determine the tour of each vehicle. We will show that our algorithm presents good solutions

Keywords:

Capacitated location allocation routing problem , Intelligent K-means algorithm , clustering , ANOVA test