Abstract
The Vehicle Routing Problem of Logistics and Distribution is a hot and difficult issue in current field of combinatorial optimization, therefore this paper presents an improved genetic algorithm. The algorithm which applied the idea of Saving Algorithm to the initialization of groups, and improved algorithm on selection operator and cross operator, In the meantime, it proposes a new way to calculate the adaptive probability in the cross operator. In addition, it also introduces a novel CX crossover operator .By the way of simulating experiments of the Vehicle Routing Problem, it demonstrates that the improved genetic algorithm enhanced the ability of global optimization, moreover it can significantly speed up convergence efficiency.
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References
Jiang, B.: Study of Vehicle Routing Problem with Time Windows Based on Genetic Algorithm. Beijing Jiaotong University (2010)
Cai, Z.: Research on logistics vehicle routing problem based on genetic algorithm. In: 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 232–235 (2011)
Huang, X., Zou, S., Zhang, H.: Improved genetic algorithm and its application to the optimization of physical distribution routing. Journal of Southwest University for Nationalities (Natrual Science Edition) 34(4) (2008)
Ren, C., Wang, X.: Research on Vehicle Scheduling Problem Based on Improved Genetic Algorithm for Electronic Commerce. In: 2007 Second IEEE Conference on Industrial Electronics and Applications, pp. 1887–1891 (2007)
Gong, G., Hu, X.-T., Wei, K.-X., Hao, G.-S.: Optimized Performance Research of the Vehicle Routing Problem in Industry Logistics. Computer Science & Engineering (2011)
Lang, M.X.: Study of the optimizing of physical distribution routing problem based on genetic algorithm. China Journal of Highway and Transport 15(3) (2002)
Zhang, Y.-M.: Research on Routing Optimization of Logistics Distribution Based on Saving Algorithm and its Improved. South China Normal University of Zengcheng College, Guangzhou (2011)
Lang, M.: Vehicle routing problem model and algorithm. Publishing House of Electronics Industry, Beijing (2009)
Kunkel, M., Schwind, M.: Vehicle Routing with Driver Learning for Real World CEP Problems. In: 2012 45th Hawaii International Conference on System Science (HICSS), pp. 1315–1322 (2012)
Calvete, H.I., Gale, C., Oliveros, M.J.: A goal programming approach to vehicle routing problems with soft time windows. European Journal of Operational Research 177, 1720–1733 (2007)
Alvarenga, G.B., Mateus, G.R., de Tomi, G.: A genetic and set portioning two-phase approach for the vehicle routing problem with time windows. Computers & Operations Research 34, 1561–1584 (2007)
Ge, H., Wang, Y.: Improved simulated annealing genetic algorithm for VRPSDP problem. Computer Engineering and Applications 46(30), 36–39 (2010)
Nazif, H., Lee, L.S.: Optimized crossover genetic algorithm for vehiclerouting problem with time windows. Amer. J. Appl. Sci. 7(1), 95–101 (2010)
Ghoseiri, K., Ghannadpour, S.F.: Hybrid genetic algorithm for vehicle routing and scheduling problem. J. Appl. Sci. 9(1), 79–87 (2009)
Kulkarni, R.V., Venayagamoorthy, G.K.: Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans.Syst., Man, Cybern. C, Appl. Rev. 40(6), 663–675 (2010)
Amini, S., Javanshir, H., Tavakkoli-Moghaddam, R.: A PSO approach for solving VRPTW with real case study. Int. J. Res. Rev. Appl. Sci. 4(3), 118–126 (2010)
Cheng, L., Wang, J.: Improved gentic algorithm for vehicle routing problem. Computer Engineering and Applications 46(36), 219–221 (2010)
Zhang, J., Fang, W.: Improved gentic algorithm for vehicle routing problem with time window. Computer Engineering and Applications 46(32), 228–231 (2010)
Gendreau, M., Tarantilis, C.D.: Solving large-scale vehicle routing problems with time windows - the state-of-the-art. Technical report, Universite de Montreal, CIRRELT (2010)
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Huang, S., Fu, X., Chen, P., Ge, C., Teng, S. (2013). An Application Study on Vehicle Routing Problem Based on Improved Genetic Algorithm. In: Zu, Q., Hu, B., Elçi, A. (eds) Pervasive Computing and the Networked World. ICPCA/SWS 2012. Lecture Notes in Computer Science, vol 7719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37015-1_20
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DOI: https://doi.org/10.1007/978-3-642-37015-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37014-4
Online ISBN: 978-3-642-37015-1
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