论文标题
通过使用二进制k均值聚类，通过二进制限制来优化计划服务领域
Optimizing Planning Service Territories by Dividing Into Compact Several Sub-areas Using Binary K-means Clustering According Vehicle Constraints
论文作者
论文摘要
VRP（车辆路由问题）是一个NP硬问题，它引起了很多研究兴趣。在车辆承载能力有限的情况下，例如体积和重量，但需要在各个位置交付物品。最初，在创建路线之前，每辆车都需要一组不超过其最大容量的交付点。驾驶员倾向于仅交付某些区域。基于群集是为生成更紧密路线的基础的方法之一。在本文中，我们提出了用于生产不超过车辆最大容量的簇/组的新算法。我们的基本假设是每辆车源自仓库，将物品交付给客户并返回仓库，车辆也是同质的。该方法能够在每个群集中紧凑下次分会。计算结果证明了我们的新程序的有效性，这些程序能够帮助用户更有效地计划服务领土和车辆路线。
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.
