摘要
以集团分公司多专业集货中心模式下为研究背景,考虑了具有多个集货中心、多种轿运车型的批量整车订单运输路径规划问题。建立了以最小运输车数量,最低运输成本为优化目标,以轿运车载重、车长限制为约束的数学模型。然后以遗传算法为基础,结合遗传算法全局搜索能力强和粒子群算法收敛速度快的特点,设计了一种改进的混合遗传算法(GA-PSO)。最后使用案例研究来评估此算法的有效性并将实验结果与传统遗传算法进行对比,结果表明此混合算法有着更优的整体性能,可以模拟和解决较复杂的批量多车场多车型整车路径规划问题。
The research background of this article is based on the multi-professional depots,this passage solves the problem of the batch vehicle order transportation route planning problem with multiple depots and multiple transport vehicle models.A mathematical model is established with the minimum number of vehicles and the minimum cost of transportation as the optimization goal,considering the constraints of load capacity and transport vehicle lengths.Then,based on the genetic algorithm,an improved hybrid genetic algorithm(GA-PSO)is designed,combined with the strong global search ability of genetic algorithm and the fast convergence speed of particle swarm algorithm.Finally,a case study is used to evaluate the effectiveness of the algorithm and the experimental results are compared with the traditional genetic algorithm.The results show that the hybrid algorithm has better overall performance and can simulate and solve the complex vehicle routing problem with multiple vehicles and multiple depots.
作者
刘建胜
谭文越
LIU Jian-sheng;TAN Wen-yue(School of Mechanical and Electronic Engineering,Nanchang University,Jiangxi Nanchang330031,China)
出处
《机械设计与制造》
北大核心
2020年第11期236-240,共5页
Machinery Design & Manufacture
基金
国家自然科学基金(51565036)。
