Heuristic methods using variable neighborhood random local search for the clustered traveling salesman problem
DOI:
https://doi.org/10.14488/1676-1901.v14i4.1721Keywords:
Operations Research. Combinatorial Optimization. Heuristic Methods. Variable Neighborhood Randomized Descent. Iterated Local Search.Abstract
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.Downloads
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Published
2014-11-15
How to Cite
Mestria, M. (2014). Heuristic methods using variable neighborhood random local search for the clustered traveling salesman problem. Revista Produção Online, 14(4), 1511–1536. https://doi.org/10.14488/1676-1901.v14i4.1721
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