IDEAS home Printed from https://ideas.repec.org/p/iim/iimawp/wp02118.html
   My bibliography  Save this paper

Implementing Tabu Search to Exploit Sparsity in ATSP Instances

Author

Listed:
  • Sumanta Basu
  • Ravindra S Gajulapalli
  • Ghosh, Diptesh

Abstract

Real life traveling salesman problem (TSP) instances are often large,sparse, and asymmetric. Conventional tabu search implementations for the TSP that have been reported in the literature, almost always deals with small, dense and symmetric instances. In this paper, we outline data structures and a tabu search implementation that takes advantage of such data structures, which can exploit sparsity of a TSP instances, and hence can solve relatively large TSP instances (with up to 3000 nodes) much faster than conventional implementations. We also provide computational experiences with this implementation.

Suggested Citation

  • Sumanta Basu & Ravindra S Gajulapalli & Ghosh, Diptesh, 2008. "Implementing Tabu Search to Exploit Sparsity in ATSP Instances," IIMA Working Papers WP2008-10-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
  • Handle: RePEc:iim:iimawp:wp02118
    as

    Download full text from publisher

    File URL: https://www.iima.ac.in/sites/default/files/rnpfiles/2008-10-02Basu.pdf
    File Function: English Version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    2. Sumanta Basu & Ghosh, Diptesh, 2008. "A review of the Tabu Search Literature on Traveling Salesman Problems," IIMA Working Papers WP2008-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marianov, Vladimir & Serra, Daniel & ReVelle, Charles, 1999. "Location of hubs in a competitive environment," European Journal of Operational Research, Elsevier, vol. 114(2), pages 363-371, April.
    2. Chiara Gruden & Irena Ištoka Otković & Matjaž Šraml, 2020. "Neural Networks Applied to Microsimulation: A Prediction Model for Pedestrian Crossing Time," Sustainability, MDPI, vol. 12(13), pages 1-22, July.
    3. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    4. Thibaud Deguilhem & Juliette Schlegel & Jean-Philippe Berrou & Ousmane Djibo & Alain Piveteau, 2024. "Too many options: How to identify coalitions in a policy network?," Post-Print hal-04689665, HAL.
    5. Anurag Agarwal, 2009. "Theoretical insights into the augmented-neural-network approach for combinatorial optimization," Annals of Operations Research, Springer, vol. 168(1), pages 101-117, April.
    6. Helena Ramalhinho-Lourenço & Olivier C. Martin & Thomas Stützle, 2000. "Iterated local search," Economics Working Papers 513, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Marti, Rafael, 1998. "A tabu search algorithm for the bipartite drawing problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 558-569, April.
    8. Mohammad Javad Feizollahi & Igor Averbakh, 2014. "The Robust (Minmax Regret) Quadratic Assignment Problem with Interval Flows," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 321-335, May.
    9. Nha Vo‐Thanh & Hans‐Peter Piepho, 2023. "Generating designs for comparative experiments with two blocking factors," Biometrics, The International Biometric Society, vol. 79(4), pages 3574-3585, December.
    10. Сластников С.А., 2014. "Применение Метаэвристических Алгоритмов Для Задачи Маршрутизации Транспорта," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(1), pages 117-126, январь.
    11. Hanafi, Said & Freville, Arnaud, 1998. "An efficient tabu search approach for the 0-1 multidimensional knapsack problem," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 659-675, April.
    12. Grolimund, Stephan & Ganascia, Jean-Gabriel, 1997. "Driving Tabu Search with case-based reasoning," European Journal of Operational Research, Elsevier, vol. 103(2), pages 326-338, December.
    13. repec:dar:wpaper:62383 is not listed on IDEAS
    14. Minghe Sun, 2007. "A Primogenitary Linked Quad Tree Approach for Solution Storage and Retrieval in Heuristic Binary Optimization," Working Papers 0009, College of Business, University of Texas at San Antonio.
    15. Bolte, Andreas & Thonemann, Ulrich Wilhelm, 1996. "Optimizing simulated annealing schedules with genetic programming," European Journal of Operational Research, Elsevier, vol. 92(2), pages 402-416, July.
    16. Jack Brimberg & Pierre Hansen & Nenad Mladenović & Eric D. Taillard, 2000. "Improvements and Comparison of Heuristics for Solving the Uncapacitated Multisource Weber Problem," Operations Research, INFORMS, vol. 48(3), pages 444-460, June.
    17. Deng, Ye & Wu, Jun & Tan, Yue-jin, 2016. "Optimal attack strategy of complex networks based on tabu search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 74-81.
    18. Drexl, Andreas & Haase, Knut, 1996. "Fast approximation methods for sales force deployment," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 411, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    19. Rego, Cesar & Roucairol, Catherine, 1995. "Using Tabu search for solving a dynamic multi-terminal truck dispatching problem," European Journal of Operational Research, Elsevier, vol. 83(2), pages 411-429, June.
    20. Chen, Lu & Langevin, André & Riopel, Diane, 2011. "A tabu search algorithm for the relocation problem in a warehousing system," International Journal of Production Economics, Elsevier, vol. 129(1), pages 147-156, January.
    21. H. A. J. Crauwels & C. N. Potts & L. N. Van Wassenhove, 1998. "Local Search Heuristics for the Single Machine Total Weighted Tardiness Scheduling Problem," INFORMS Journal on Computing, INFORMS, vol. 10(3), pages 341-350, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iim:iimawp:wp02118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/eciimin.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.