Academic Journal of Computing & Information Science, 2019, 2(1); doi: 10.25236/AJCIS.010030.
JI XueJing, YONG Xu
Northeastern University, information and computing science, 1501,Shenyang, Liaoning, 110004 China
This paper considers genetic algorithm to process logistics path optimization. Genetic algorithms are significant in location issues, distribution issues, scheduling issues, transportation issues, and layout issues. A genetic algorithm for solving this problem is constructed base on the mathematical model of logistics distribution path optimization problem. The calculation results show that using genetic algorithm to optimize the logistics distribution path can easily and effectively obtain the optimal solution or approximate optimal solution.
Genetic algorithm; Data mining; Logistics path optimization; Binary code
JI XueJing, YONG Xu, Application of Genetic Algorithm in Logistics Path Optimization. Academic Journal of Computing & Information Science (2019) Vol. 2: 155-161. https://doi.org/10.25236/AJCIS.010030.
[1] Dantzig, George Bernard; Ramser, John Hubert (October 1959). The Truck Dispatching Problem. Management Science. 6 (1): 80–91.
[2] Li Xiangyang.Genetic Algorithms for VRP Problem[J].Computer Engineering and Design, 2004,25(2): 271-276. [3] Paolo Toth，Daniele Vigo. THE VEHICLE ROUTING PROBLEM[M]. Society for Industrial and Applied Mathematics philadephia.2002.
[4] Zhu Chongjun, Liu Min, Wu Cheng. Research Progress and Prospect of Vehicle Routing Problem in Supply Chain [J]. Computer Integrated Manufacturing System－CMS.2001,7(11):1-6.
[5] MBA think tank encyclopedia. Vehicle routing problem [EB/OL]. https://wiki.mbalib.com/wiki/vehicle routing problem,2016-11-09.
[6] J.H.Holland. Adaptation in Natural and Artificial Systems[M]. University of Michigan Press, Ann Arbor, 1976.
[7] Li Zhengyan. Research on Logistics Transportation System Based on Data Mining [D]. Chongqing University,2010.
[8] Tian Shaoliang, Zuo Ming and Wu Shaowei. An improved load balancing algorithm based on dynamic feedback [J]. Computer engineering and design. 2007. 28(3): 572-573.
[9] Peiming. Research on Some Key Problems of Massive Data Processing in Grid Environment [D]. Beijing: Institute of High Energy Physics, Chinese Academy of Sciences,2007: 72-75.