Ant colony optimization with clustering for solving the dynamic location routing problem

Shangce Gao; Yirui Wang; Jiujun Cheng; Yasuhiro Inazumi; Yuki Todo

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Ant colony optimization with clustering for solving the dynamic location routing problem

Shangce Gao 1,Yirui Wang 2,Jiujun Cheng 3,Yasuhiro Inazumi 4,Yuki Todo 5

1.College of Information Science and Technology, Donghua University, Shanghai, China 201620; Graduate School of Innovative Life Science, University of Toyama, Toyama-shi, Japan 930-8555

2.College of Information Science and Technology, Donghua University, Shanghai, China 201620

3.The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China 200092

4.Graduate School of Innovative Life Science, University of Toyama, Toyama-shi, Japan 930-8555

5.School of Electrical and Computer Engineering, Kanazawa University, Kanazawa-shi, Japan 920-1192

*Correspondence author

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Funding: Shanghai Rising-Star Program （No.No. 14QA1400100）, ``Chen Guang” project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation （No.No. 12CG35）, the Fundamental Research Funds for the Central Universities （No.No. 2232013D3-39）, Ph.D. Program Foundation of Ministry of Education of China （No.No. 20120075120004）, National Natural Science Foundation of China （No.Grants No. 61203325）

Opened online: 8 July 2015

Accepted by: none

Citation: Shangce Gao,Yirui Wang,Jiujun Cheng.Ant colony optimization with clustering for solving the dynamic location routing problem[OL]. [ 8 July 2015] http://en.paper.edu.cn/en_releasepaper/content/4649159

Ant colony algorithm can resolve dynamic optimization problems due to its robustness and adaptation. The aim of such algorithms in dynamic environments is no longer to find an optimal solution but to trail it over time. In this paper, a clustering ant colony algorithm with three immigrants schemes (K-ACS) is proposed to address the dynamic location routing problem (LRP). The LRP is divided into two parts constituted by a location allocation problem (LAP) and a vehicles routing problem (VRP) in dynamic environments. To deal with the LAP, a K-means clustering algorithm is used to tackle the location of depots and surrounding cities in each class. Then the ant colony algorithm with three immigrants including random immigrants, elitism-based immigrants and memory-based immigrants is utilized to handle the VRP in dynamic environments consisting of random and cyclic traffic factors. A comparative study is carried out to assess the effect of the utilization of clustering. Experimental results based on different scales of LRP instances demonstrate that the clustering algorithm can significantly improve the performance of K-ACS in terms of the qualities and robustness of solutions. Furthermore, K-ACS show promising performance in solving the LRP in two different dynamic environments, suggesting that the proposed algorithm may lead to a new technique for tracking the environmental changes by utilizing its clustering and evolutionary characteristics.

Keywords:Ant colony algorithm, clustering algorithm, immigrants schemes, location routing.

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