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Discrete Dynamics in Nature and Society
Volume 2018, Article ID 1295485, 13 pages
https://doi.org/10.1155/2018/1295485
Research Article

Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

Correspondence should be addressed to Haitao Xu; nc.ude.udh@oatiahux

Received 20 October 2017; Revised 17 January 2018; Accepted 21 January 2018; Published 15 February 2018

Academic Editor: Gabriella Bretti

Copyright © 2018 Haitao Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP). Dynamic vehicle routing problem (DVRP) is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO), which is the traditional Ant Colony Optimization (ACO) fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.