Approximation Schemes for the Generalized Traveling Salesman Problem

Article

Abstract

The Generalized Traveling Salesman Problem (GTSP) is defined by a weighted graph G = (V,E,w) and a partition of its vertex set into k disjoint clusters V = V1 ∪... ∪ Vk. It is required to find a minimum-weight cycle that contains exactly one vertex of each cluster. We consider a geometric setting of the problem (we call it the EGTSP-k-GC), in which the vertices of the graph are points in the plane, the weight function corresponds to the Euclidean distances between the points, and the partition into clusters is specified implicitly by means of a regular integer grid with step 1. In this setting, a cluster is a subset of vertices lying in the same cell of the grid; the arising ambiguity is resolved arbitrarily. Even in this special setting, the GTSP remains intractable, generalizing in a natural way the classical planar Euclidean TSP. Recently, a \((1.5 + 8\sqrt 2 + \varepsilon )\) -approximation algorithm with complexity depending polynomially both on the number of vertices n and on the number of clusters k has been constructed for this problem. We propose three approximation schemes for this problem. For each fixed k, all the schemes are polynomial and the complexity of the first two is linear in the number of nodes. Furthermore, the first two schemes remain polynomial for k = O(log n), whereas the third scheme is polynomial for k = n − O(log n).

Keywords

generalized traveling salesman problem NP-hard problem polynomial-time approximation scheme. 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    A. N. Sesekin, A. A. Chentsov, and A. G. Chentsov, Routing Problems (Lan’, St. Petersburg, 2011) [in Russian].MATHGoogle Scholar
  2. 2.
    E. M. Arkin and R. Hassin, “Approximation algorithms for the geometric covering salesman problem,” Discrete Appl. Math. 55 (3), 197–218 (1994).MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    S. Arora, “Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems,” J. ACM 45 (5), 753–782 (1998).MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    B. Bhattacharya, A. ´Custi´c, A. Rafiey, A. Rafiey, and V. Sokol, “Approximation algorithms for generalized MST and TSP in grid clusters,” in Combinatorial Optimization and Applications: Proceedings of the 9th International Conference, Houston, TX, USA, 2015 (Springer, Cham, 2015), pp. 110–125.CrossRefGoogle Scholar
  5. 5.
    B. Bontoux, C. Artigues, and D. Feillet, “A memetic algorithm with a large neighborhood crossover operator for the generalized traveling salesman problem,” Comput. Oper. Res. 37 (11), 1844–1852 (2010).MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    A. Chentsov, M. Khachay, and D. Khachay, “Linear time algorithm for Precedence Constrained Asymmetric Generalized Traveling Salesman Problem,” IFAC-PapersOnLine 49 (12), 651–655 (2016).CrossRefGoogle Scholar
  7. 7.
    N. Christofides, Worst-Case Analysis of a New Heuristic for the Traveling Salesman Problem, Technical Report No. AD-A025 602 (Carnegie Mellon Univ. Pittsburgh, 1976).Google Scholar
  8. 8.
    M. Dror and J. Orlin, “Combinatorial optimization with explicit delineation of the ground set by a collection of subsets,” SIAM J. Discrete Math. 21 (4), 1019–1034 (2008).MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    A. Dumitrescu and J. S. B. Mitchell, “Approximation algorithms for TSP with neighborhoods in the plane,” in Proceedings of the 12th Annual ACM–SIAM Symposium on Discrete Algorithms, Washington, DC, USA, 2001 (SIAM, Philadelphia, 2001), pp. 38–46.Google Scholar
  10. 10.
    A. Dumitrescu and C. D. Tóth, “The traveling salesman problem for lines, balls, and planes,” ACM Trans. Algorithms 12 (3), article 43 (2016).Google Scholar
  11. 11.
    C. Feremans, A. Grigoriev, and R. Sitters, “The geometric generalized minimum spanning tree problem with grid clustering,” 4OR 4 (4), 319–329 (2006).MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    G. Gutin and D. Karapetyan, “A memetic algorithm for the generalized traveling salesman problem,” Nat. Comput. 9 (1), 47–60 (2010).MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    M. Held and R. M. Karp, “A dynamic programming approach to sequencing problems,” J. Soc. Indust. Appl. Math. 10 (1), 196–210 (1962).MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    A. Henry-Labordere, “The record balancing problem: A dynamic programming solution of a generalized traveling salesman problem,” RAIRO Oper. Res. B2, 43–49 (1969).MATHGoogle Scholar
  15. 15.
    K. Jun-man and Z. Yi, “Application of an improved ant colony optimization on generalized traveling salesman problem,” Energy Procedia 17A, 319–325 (2012).CrossRefGoogle Scholar
  16. 16.
    G. Laporte, H. Mercure, and Y. Nobert, “Generalized travelling salesman problem through n sets of nodes: The asymmetrical case,” Discrete Appl. Math. 18 (2), 185–197 (1987).MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    C. S. Mata and J. S. B. Mitchell, “Approximation algorithms for geometric tour and network design problems,” in Proceedings of the 11th Annual Symposium on Computational Geometry, Vancouver, Canada, 1995 (ACM, New York, 1995), pp. 360–369.Google Scholar
  18. 18.
    J. S. B. Mitchell, “A PTAS for TSP with neighborhoods among fat regions in the plane,” in Proceedings of the 18th Annual ACM–SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA, 2007 (SIAM, Philadelphia, 2007), pp. 11–18.Google Scholar
  19. 19.
    J. Saksena, “Mathematical model for scheduling clients through welfare agencies,” CORS J. 8, 185–200 (1970).MathSciNetMATHGoogle Scholar
  20. 20.
    D. Williamson and D. Shmoys, The Design of Approximation Algorithms, (Cambridge Univ. Press, Cambridge, 2010).MATHGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Krasovskii Institute of Mathematics and MechanicsUral Branch of the Russian Academy of SciencesYekaterinburgRussia
  2. 2.Ural Federal UniversityYekaterinburgRussia
  3. 3.Omsk State Technical UniversityOmskRussia

Personalised recommendations