Balanced K-Means Algorithm for Partitioning Areas in Large-Scale Vehicle Routing Problem

被引：24
|
作者：
He, Ruhan [1 ]
Xu, Weibin [2 ]
Sun, Jiaxia [3 ]
Zu, Bingqiao [2 ]
机构：
[1] Wuhan Univ Sci & Engn, Coll Comp Sci, Wuhan 430073, Peoples R China
[2] Hubei Prov Tobacco Monopoly Bur, Wuhan 430030, Peoples R China
[3] Henan Inst Sci & Technol, Sch Informat Engn, Xinxiang 453003, Henan, Peoples R China
来源：
2009 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL 3, PROCEEDINGS | 2009年
关键词：
Vehicle Routing Problem (VRP); K-Means; Partitioning Area; GUIDED EVOLUTION STRATEGIES;
D O I：
10.1109/IITA.2009.307
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We present a new and effective algorithm, balanced k-means, for partitioning areas in large-scale vehicle routing problem (VRP). The algorithm divides two-stage procedures. The traditional k-means is used to partition the whole customers into several areas in the first stage and a border adjustment algorithm aims to adjust the unbalanced areas to be balanced in the second stage. The objective of partitioning areas is to design a group of geographically closed customers with balanced number of customers. The presented algorithm is specifically designed for large-scale problems based on decomposition strategy. The computational experiments were carried out on a real dataset with 1882 customers. The results demonstrate that the suggested method is highly competitive, providing the balanced areas in real application.
引用
收藏
页码：87 / +
页数：2
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