摘要
探讨车辆调度问题的解决方法,提出一种用于求解带容量约束的多车调度问题(CVRP)的混合优化算法.该算法分为路线划分、构造初始解和改进解3个阶段:第1阶段用模糊C均值聚类算法将所有客户按车容量要求装车;第2阶段用暂态混沌神经网络方法对每条路线排序;第3阶段用禁忌搜索法改进得到的解.最后采用标准问题进行仿真计算,通过与其他算法的比较,说明该算法是求解CVRP问题可行且高效的方法.
A novel approximation algorithm for the capacity vehicle routing problem （CVRP） is proposed to find the minimum total cost of all routes. The new algorithm is composed of three parts. Firstly, fuzzy C-mean clustering （FCM） selects the vehicles for customers according to the capacity of the vehicle and the similar feature among customers. Then, transiently chaotic neural network （TCNN） searches the initial optimal routes for the selected fleet and tabu search （TS） improves the solution. Computions on benchmark problems and comparisons with other algorithms show the feasibility and effectiveness of the proposed algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2007年第1期105-108,112,共5页
Control and Decision
基金
国家自然科学基金项目(60174024)
关键词
车辆调度
模糊C均值聚类
暂态混沌神经网络
禁忌搜索
混合优化算法
Capacity vehicle routing problem
Fuzzy C-mean clustering Transiently chaotic neural network
Tabu search
Hybrid optimization algorithm
