摘要
带容量约束的多车调度问题是典型的NP-hard问题,利用模糊C均值聚类算法的相似性分类原理及混沌神经网络的全局搜索能力和高搜索效率,提出了一种快速且易于实现的新的混合启发式算法.该算法分为用模糊C均值聚类算法将所有客户按车容量要求装车和用暂态混沌神经网络方法对每条路线排序两个阶段.实例计算以及与其他算法比较表明,该算法是一种求解多车调度问题的可行且高效的方法.
Capacity vehicle routing problem （CVRP） is an NP-hard problem. A novel approximation algorithm was presented for the problem of finding the minimum total cost of all routes in CVRP environment. The new algorithm is based on the principle of fuzzy C-means （FCM） clustering algorithm and the transiently chaotic neural network （TCNN） algorithm. FCM can group the customers with close Euclidean distance into the same vehicle according to the principle of similar feature partition, firstly. TCNN combines local search and global search, possessing high search efficiency. It will solve the routes to optimality. The computation results show that the proposed algorithm is a viable and effective approach for CVRP.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2006年第7期1148-1151,共4页
Journal of Shanghai Jiaotong University
关键词
车辆调度
模糊C均值聚类
暂态混沌神经网络
混合优化算法
capacity vehicle routing problem（CVRP）
fuzzy C-means
transiently chaotic neural network （TCNN）
hybrid optimization algorithm
