摘要
研究了云计算条件下物流车辆路径规划问题,提出了一种基于改进遗传算法的路径规划方法。该方法将目标函数定义为收货方满意度、配送成本和配送时间之和,结合实际问题构建了目标函数的约束条件。针对遗传算法收敛速度慢和局部最优问题,对遗传算法的选择、交叉和变异操作进行改进,提升路径规划的寻优速度和寻优质量。测试结果表明,该方法能够有效完成云计算条件下的物流车辆路径规划,且车辆调度结果较优。
Problem of logistics vehicle routing under cloud computing is studied,and an improved genetic algorithm based route planning method is proposed.In this method,the objective function is defined as the sum of consignee satisfaction,distribution cost and distribution time,and the constraints of the objective function are constructed in combination with practical problems.Aiming at the slow convergence speed and local optimum of genetic algorithm,the selection,crossover and mutation operation of genetic algorithm are improved to improve the speed and quality of path planning.Test results show that the method can effectively complete the logistics vehicle routing planning under cloud computing conditions,and the vehicle scheduling results are better.
作者
林美
何竹峰
Lin Mei;He Zhufeng(Guangdong Southern Vocational College,Jiangmen Guangdong 529000,China)
出处
《电子测量技术》
2019年第13期153-156,共4页
Electronic Measurement Technology
关键词
物流配送
车辆路径规划
云计算
遗传算法
Logistics Distribution
Vehicle Route Planning
Cloud Computing
Genetic Algorithms
