Balanced K-means algorithm for partitioning areas in large-scale vehicle routing problem

Authors:
Ruhan He;Weibin Xu;Jiaxia Sun;Bingqiao Zu
Affiliations:
College Computer Science, Wuhan University of Science and Engineering, Wuhan, P. R. China;Hubei Provincial Tobacco Monopoly Bureau, Wuhan, P. R. China;School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, Henan Province, P. R. China;Hubei Provincial Tobacco Monopoly Bureau, Wuhan, P. R. China
Venue:
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Year:
2009

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Abstract

We present a new and effective algorithm, balanced k-means, for partitioning areas in large-scale vebicle 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 geograpbically dosed 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.