Heuristic methods using variable neighborhood random local search for the clustered traveling salesman problem

Heuristic methods using variable neighborhood random local search for the clustered traveling salesman problem

In this paper, we propose new heuristic methods for solver the Clustered Traveling Salesman Problem (CTSP). The CTSP is a generalization of the Traveling Salesman Problem (TSP) in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycl...

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Bibliographic Details
Main Author: Mário Mestria
Format: Article
Language:English
Published: Universidade Federal de Santa Catarina 2014-11-01
Series:Revista Produção Online
Subjects:
Pesquisa Operacional. Otimização Combinatória. Métodos Heurísticos. Método de Descida Aleatória em Vizinhança Variável. Iterated Local Search.
Online Access:http://producaoonline.org.br/rpo/article/view/1721
Description
Summary:In this paper, we propose new heuristic methods for solver the Clustered Traveling Salesman Problem (CTSP). The CTSP is a generalization of the Traveling Salesman Problem (TSP) in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. We develop two Variable Neighborhood Random Descent with Iterated Local for solver the CTSP. The heuristic methods proposed were tested in types of instances with data at different level of granularity for the number of vertices and clusters. The computational results showed that the heuristic methods outperform recent existing methods in the literature and they are competitive with an exact algorithm using the Parallel CPLEX software.
ISSN:1676-1901

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