Optimizing Planning Service Territories by Dividing Into Compact Several Sub-areas Using Binary K-means Clustering According Vehicle Constraints

TL;DR

This paper introduces a novel binary K-means clustering algorithm tailored for vehicle routing problems, effectively creating compact service territories that respect vehicle capacity constraints, thereby enhancing route planning efficiency.

Contribution

The paper presents a new clustering method that produces capacity-constrained, compact sub-areas for vehicle routing, improving upon existing cluster-based approaches.

Findings

Clusters are more compact and capacity-compliant.

Route planning efficiency is significantly improved.

Method outperforms traditional clustering techniques.

Abstract

VRP (Vehicle Routing Problem) is an NP hard problem, and it has attracted a lot of research interest. In contexts where vehicles have limited carrying capacity, such as volume and weight but needed to deliver items at various locations. Initially before creating a route, each vehicle needs a group of delivery points that are not exceeding their maximum capacity. Drivers tend to deliver only to certain areas. Cluster-based is one of the approaches to give a basis for generating tighter routes. In this paper we propose new algorithms for producing such clusters/groups that do not exceed vehicles maximum capacity. Our basic assumptions are each vehicle originates from a depot, delivers the items to the customers and returns to the depot, also the vehicles are homogeneous. This methods are able to compact sub-areas in each cluster. Computational results demonstrate the effectiveness of our new procedures, which are able to assist users to plan service territories and vehicle routes more efficiently.