Let G = （V, E） be a complete undirected graph with vertex set V, edge set E, and edge weights I（e） satisfying the triangle inequality. The vertex set V is partitioned into clusters V1, V2 ,…, Vk. The clustered tr...Let G = （V, E） be a complete undirected graph with vertex set V, edge set E, and edge weights I（e） satisfying the triangle inequality. The vertex set V is partitioned into clusters V1, V2 ,…, Vk. The clustered traveling salesman problem （CTSP） seeks to compute the shortest Hamiltonian tour that visits all the vertices, in which the vertices of each cluster are visited consecutively. A two-level genetic algorithm （TLGA） was developed for the problem, which favors neither intra-cluster paths nor inter-cluster paths, thus realized integrated evolutionary optimization for both levels of the CTSP. Results show that the algorithm is more effective than known algorithms. A large-scale traveling salesman problem （TSP） can be converted into a CTSP by clustering so that it can then be solved by the algorithm. Test results demonstrate that the clustering TLGA for large TSPs is more effective and efficient than the classical genetic algorithm.展开更多
The research of efficient computation focus on special structures of NP-hard problem instances and request providing reasonable computing cost of instances in polynomial time. Based on the theory of combinatorial opti...The research of efficient computation focus on special structures of NP-hard problem instances and request providing reasonable computing cost of instances in polynomial time. Based on the theory of combinatorial optimization, by studying the clusters partition and the clusters complexity measurement in Nvehicle exploration problem, we build a frame of efficient computation and provide an application of tractability for NP-hard problem. Three N-vehicle examples show that when we use efficient computation mechanism on N-vehicle, through polynomial steps of tractability analysis, decision makers can get the computing cost of searching optimal solution before practical calculation.展开更多
Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary ...Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem. The aim of thisresearch is to solve large-scale DVRP (LSDVRP) in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle. In LSDVRP, it isdifficult to get an exact solution and the computational time complexity growsexponentially. To find near-optimal answers for this problem, a hierarchicalapproach consisting of three stages: “clustering, route-construction, routeimprovement”is proposed. The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results. The resultsconfirmed that the proposed methodology is applicable.展开更多
文摘Let G = （V, E） be a complete undirected graph with vertex set V, edge set E, and edge weights I（e） satisfying the triangle inequality. The vertex set V is partitioned into clusters V1, V2 ,…, Vk. The clustered traveling salesman problem （CTSP） seeks to compute the shortest Hamiltonian tour that visits all the vertices, in which the vertices of each cluster are visited consecutively. A two-level genetic algorithm （TLGA） was developed for the problem, which favors neither intra-cluster paths nor inter-cluster paths, thus realized integrated evolutionary optimization for both levels of the CTSP. Results show that the algorithm is more effective than known algorithms. A large-scale traveling salesman problem （TSP） can be converted into a CTSP by clustering so that it can then be solved by the algorithm. Test results demonstrate that the clustering TLGA for large TSPs is more effective and efficient than the classical genetic algorithm.
基金Supported by Key Laboratory of Management,Decision and Information Systems,Chinese Academy of Science
文摘The research of efficient computation focus on special structures of NP-hard problem instances and request providing reasonable computing cost of instances in polynomial time. Based on the theory of combinatorial optimization, by studying the clusters partition and the clusters complexity measurement in Nvehicle exploration problem, we build a frame of efficient computation and provide an application of tractability for NP-hard problem. Three N-vehicle examples show that when we use efficient computation mechanism on N-vehicle, through polynomial steps of tractability analysis, decision makers can get the computing cost of searching optimal solution before practical calculation.
文摘Companies are eager to have a smart supply chain especially when they have adynamic system. Industry 4.0 is a concept which concentrates on mobility andreal-time integration. Thus, it can be considered as a necessary component thathas to be implemented for a dynamic vehicle routing problem. The aim of thisresearch is to solve large-scale DVRP (LSDVRP) in which the delivery vehiclesmust serve customer demands from a common depot to minimize transit costswhile not exceeding the capacity constraint of each vehicle. In LSDVRP, it isdifficult to get an exact solution and the computational time complexity growsexponentially. To find near-optimal answers for this problem, a hierarchicalapproach consisting of three stages: “clustering, route-construction, routeimprovement”is proposed. The major contribution of this paper is dealing withLSDVRP to propose the three-stage algorithm with better results. The resultsconfirmed that the proposed methodology is applicable.
