Research Article | OPEN ACCESS
Multiple-depot Food Transport Vehicle Routing Genetic Algorithm Based on Two-stage Fuzzy Clustering
1Qiang Song, 1He Feng and 2Lingxia Liu
1Computer School, Anyang Institute of Technology
2School of Software Engineering, Anyang Normal University, Anyang City, 455000, China
Advance Journal of Food Science and Technology 2016 1:31-36
Received: April ‎19, ‎2015 | Accepted: May ‎10, ‎2015 | Published: January 05, 2016
Abstract
Aiming to large-scale Multiple-Depot Food transport Vehicle Routing Problem (MDFVRP), this study proposed an improved genetic algorithm solution frame based on the two-stage fuzzy clustering. In the static upper stage, the k-means technology is used to divide the MDFVRP into several one-to-many sub-problems. From the perspective of improving the customer satisfaction and integrating logistics resource, the lower fuzzy clustering stage adopts fuzzy clustering algorithm to form the dynamic customer base based on customer’s order distribution according to customers request attributes. Furthermore, the Genetic Algorithm (GA) of VRP is designed through the improvement of the selecting operator and the crossover operator. The stochastic simulation experiments show the proposed algorithm and solution strategy are efficient.
Keywords:
Food transport vehicle routing, improved genetic algorithm, two-stage fuzzy clustering,
References
-
Changshi, L. and L. Mingyong, 2010. The stochastic food transport vehicle routing problem based on fuzzy clustering and food transport vehicle collaboration strategy. J. Manage. Eng., 24(2): 75-78.
-
Ho, W., G.T.S. Ho, P. Ji and H.C.W. Lau, 2008. A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intel., 21(4): 548-557.
CrossRef
-
Hu, T.L. and J.B. Sheu, 2003. A fuzzy-based customer classification method for advanced demand-responsive logistical distribution operations. Fuzzy Set. Syst., 139(2): 431-450.
CrossRef
-
Huang, M., X. Hu, Z. Wang and A.Z. Zeng, 2009. A three-stage solution approach to vehicle routing problem. J. Manage. Sci., 22(3): 37-46.
-
Liying, Z. and J. Haipeng, 2009. The multiple-depot heterogeneous-food transport vehicle scheduling algorithm based on global search clustering. J. Lanzhou Jiao Tong Univ., 28(6): 19-22.
-
Mirabi, M., S.M.T.F. Ghomi and F. Jolai, 2010. Efficient stochastic hybrid heuristics for the multi-depot food transport vehicle routing problem. Robot. Cim-Int. Manuf., 26(6): 564-569.
-
Tong, Z., L. Ning and S. Debao, 2004. The genetic algorithm of multiple-depot food transport vehicle routing problem. Comput. Eng. Appl., 40(21): 81-83.
-
Wei, S.H. and S.M. Chen, 2009. A new approach for fuzzy risk analysis based on similarity measures of generalized fuzzy numbers. Expert Syst. Appl., 36(1): 581-588.
CrossRef
-
Yvcenur, G.N. and N.C. Demirel, 2011. A new geometric shape-based genetic clustering algorithm for the multi-depot food transport vehicle routing problem. Expert Syst. Appl., 38(9): 11859-11865.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2042-4876
ISSN (Print): 2042-4868 |
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