Abstract:

When exporting e-commerce enterprises to concentrate goods in the inland dry ports, considering the problems of low efficiency and high cost encountered in the transportation process, the article proposes a practical solution, that is, through the form of vehicle sharing, it reduces the cost of the shipper and makes the management of the dry port more convenient. After the improvement of the traditional cargo transportation mode, a mathematical model is established to minimize the total transportation distance of all trucks. The shipping points are first grouped by scanning method, and then the Improved Genetic Algorithm（IGA） is used for path optimization. IGA is used to solve the model and compare it with the results of traditional Genetic Algorithm（GA） and Particle Swarm Optimization（PSO） for different delivery points and the size of the collection vehicle. After MATLAB analysis of small-scale experiments, the total number of improved vehicles is 3, and the total cost of distribution is 5, 548.67 yuan. Compared with the other two traditional modes of transportation, the superiority of the method described in the article is proved.

Key words: dry port collection, vehicle sharing, Improved Genetic Algorithm（IGA）, Capacitated Vehicle Routing Problem（CVRP）

摘要：

在出口电商企业向内地无水港进行货物集中时，考虑其交通运输过程中遇到的效率低、成本高等问题，提出了一种切实可行的解决办法，即通过车辆共享的形式，既降低了发货商的成本，也使得无水港的管理更加便捷。在对传统的集货运输模式改进后，建立以最小化所有货车总运输路程的数学模型，先使用扫描法对发货点进行分组，后使用改进的遗传算法（IGA）进行路径优化。针对不同数量的发货点以及集货车辆规模，使用IGA对模型求解并与传统遗传算法（GA）以及粒子群算法（PSO）所得结果对比。经MATLAB对小规模实验进行算例分析，得到改进后的车辆需求总数为3 辆，配送总成本为5 485.67元，与另外两种传统运输方式对比，证明了所述方法的优越性。

关键词: 无水港集货, 车辆共享, 改进的遗传算法（IGA）, 车辆路径优化