Computer Engineering and Applications ›› 2015, Vol. 51 ›› Issue (21): 230-236.

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Research on vehicle logistics transportation scheduling problem

TIAN Ran, SUN Linfu, TANG Huijia, LI Binyong   

  1. CAD Center, Southwest Jiaotong University, Chengdu 610031, China
  • Online:2015-11-01 Published:2015-11-16

多车场物流协同运输调度问题研究

田  冉,孙林夫,唐慧佳,李斌勇   

  1. 西南交通大学 CAD中心,成都 610031

Abstract: Collaborative logistics transportation is the development trend of modern logistics mode, but the profit is an important factor driving the collaborative logistics. In view of  the collaborative logistics problems, this paper establishes a distribution task model based on profit driven and constraint of auto parts logistics transportation. It establishes the population genetic based on collecting goods center point through fuzzy clustering, and then iterative evolution through crossover mutation operation of genetic algorithm to get an optimal solution in a certain number of iterations. An example to illustrate the solution of the model results can be made collaboration between different transportation vehicles logistics enterprises and increase the profit at the same time which verifies the correctness and rationality of the model.

Key words: profit driven, collaborative transportation, cluster, genetic algorithm, multi-depot

摘要: 物流协同运输是现代物流模式的发展趋势,而利润则是驱动物流协同的关键因素。针对多车场物流协同运输中的调度问题,基于汽车配件物流运输的相关约束,建立了由利润驱动的配送任务模型。通过模糊聚类建立基于集货中心点的遗传种群,通过遗传算法的交叉、变异操作进行迭代进化,在一定的迭代次数内得到一个最优解。通过一个实例说明了该模型的求解结果可以使得不同物流企业的运输车辆之间发生协同的同时达到增加物流企业运输利润的目标,从而验证了该模型的正确性和合理性。

关键词: 利润驱动, 协同运输, 聚类, 遗传算法, 多车场