IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v303y2022i1p143-158.html
   My bibliography  Save this article

GRASP with strategic oscillation for the α-neighbor p-center problem

Author

Listed:
  • Sánchez-Oro, J.
  • López-Sánchez, A.D.
  • Hernández-Díaz, A.G.
  • Duarte, A.

Abstract

This paper presents a competitive algorithm that combines the Greedy Randomized Adaptive Search Procedure including a Tabu Search instead of a traditional Local Search framework, with a Strategic Oscillation post-processing, to provide high-quality solutions for the α-neighbor p-center problem (α−pCP). This problem seeks to locate p facilities to service or cover a set of n demand points with the objective of minimizing the maximum distance between each demand point and its αth nearest facility. The algorithm is compared to the best method found in the state of the art, which is an extremely efficient exact procedure for the continuous variant of the problem. An extensive comparison shows the relevance of the proposal, being able to provide competitive results independently of the α value.

Suggested Citation

  • Sánchez-Oro, J. & López-Sánchez, A.D. & Hernández-Díaz, A.G. & Duarte, A., 2022. "GRASP with strategic oscillation for the α-neighbor p-center problem," European Journal of Operational Research, Elsevier, vol. 303(1), pages 143-158.
  • Handle: RePEc:eee:ejores:v:303:y:2022:i:1:p:143-158
    DOI: 10.1016/j.ejor.2022.02.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722172200159X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2022.02.038?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thomas A. Feo & Mauricio G. C. Resende & Stuart H. Smith, 1994. "A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set," Operations Research, INFORMS, vol. 42(5), pages 860-878, October.
    2. S. L. Hakimi, 1964. "Optimum Locations of Switching Centers and the Absolute Centers and Medians of a Graph," Operations Research, INFORMS, vol. 12(3), pages 450-459, June.
    3. Mark S. Daskin & Kayse Lee Maass, 2015. "The p-Median Problem," Springer Books, in: Gilbert Laporte & Stefan Nickel & Francisco Saldanha da Gama (ed.), Location Science, edition 127, chapter 0, pages 21-45, Springer.
    4. Becky Callaghan & Said Salhi & Jack Brimberg, 2019. "Optimal solutions for the continuous p-centre problem and related -neighbour and conditional problems: A relaxation-based algorithm," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(2), pages 192-211, February.
    5. Jack Brimberg & Andrea Maier & Anita Schöbel, 2021. "When closest is not always the best: The distributed p-median problem," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(1), pages 200-216, January.
    6. Manuel Laguna & Thomas A. Feo & Hal C. Elrod, 1994. "A Greedy Randomized Adaptive Search Procedure for the Two-Partition Problem," Operations Research, INFORMS, vol. 42(4), pages 677-687, August.
    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. A. D. López-Sánchez & J. Sánchez-Oro & M. Laguna, 2021. "A New Scatter Search Design for Multiobjective Combinatorial Optimization with an Application to Facility Location," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 629-642, May.
    2. Drezner, Zvi & Eiselt, H.A., 2024. "Competitive location models: A review," European Journal of Operational Research, Elsevier, vol. 316(1), pages 5-18.
    3. Yogesh K. Agarwal, 2002. "Design of Capacitated Multicommodity Networks with Multiple Facilities," Operations Research, INFORMS, vol. 50(2), pages 333-344, April.
    4. Alejandra Casado & Sergio Pérez-Peló & Jesús Sánchez-Oro & Abraham Duarte, 2022. "A GRASP algorithm with Tabu Search improvement for solving the maximum intersection of k-subsets problem," Journal of Heuristics, Springer, vol. 28(1), pages 121-146, February.
    5. Drexl, Andreas & Salewski, Frank, 1996. "Distribution Requirements and Compactness Constraints in School Timetabling. Part II: Methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 384, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    6. Böttcher, Jan & Drexl, Andreas & Kolisch, Rainer & Salewski, Frank, 1996. "Project scheduling under partially renewable resource constraints," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 398, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    7. Sacramento Quintanilla & Francisco Ballestín & Ángeles Pérez, 2020. "Mathematical models to improve the current practice in a Home Healthcare Unit," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(1), pages 43-74, March.
    8. Raúl Martín-Santamaría & Ana D. López-Sánchez & María Luisa Delgado-Jalón & J. Manuel Colmenar, 2021. "An Efficient Algorithm for Crowd Logistics Optimization," Mathematics, MDPI, vol. 9(5), pages 1-19, March.
    9. Salewski, Frank & Bartsch, Thomas, 1994. "A comparison of genetic and greedy randomized algorithms for medium-to-short-term audit-staff scheduling," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 356, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    10. A J Higgins & L A Laredo, 2006. "Improving harvesting and transport planning within a sugar value chain," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(4), pages 367-376, April.
    11. Panos M. Pardalos & Tianbing Qian & Mauricio G.C. Resende, 1998. "A Greedy Randomized Adaptive Search Procedure for the Feedback Vertex Set Problem," Journal of Combinatorial Optimization, Springer, vol. 2(4), pages 399-412, December.
    12. Colmenar, J. Manuel & Greistorfer, Peter & Martí, Rafael & Duarte, Abraham, 2016. "Advanced Greedy Randomized Adaptive Search Procedure for the Obnoxious p-Median problem," European Journal of Operational Research, Elsevier, vol. 252(2), pages 432-442.
    13. El-Ghazali Talbi, 2016. "Combining metaheuristics with mathematical programming, constraint programming and machine learning," Annals of Operations Research, Springer, vol. 240(1), pages 171-215, May.
    14. Charles Fleurent & Fred Glover, 1999. "Improved Constructive Multistart Strategies for the Quadratic Assignment Problem Using Adaptive Memory," INFORMS Journal on Computing, INFORMS, vol. 11(2), pages 198-204, May.
    15. Marilène Cherkesly & Claudio Contardo, 2021. "The conditional p-dispersion problem," Journal of Global Optimization, Springer, vol. 81(1), pages 23-83, September.
    16. Schirmer, Andreas & Riesenberg, Sven, 1997. "Parameterized heuristics for project scheduling: Biased random sampling methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 456, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    17. Kalczynski, Pawel & Drezner, Zvi, 2022. "The Obnoxious Facilities Planar p-Median Problem with Variable Sizes," Omega, Elsevier, vol. 111(C).
    18. Serigne Gueye & Philippe Michelon, 2005. "“Miniaturized” Linearizations for Quadratic 0/1 Problems," Annals of Operations Research, Springer, vol. 140(1), pages 235-261, November.
    19. Xu Wang & Guohua Gan & Ling-Yun Wu, 2020. "Framework and algorithms for identifying honest blocks in blockchain," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-14, January.
    20. Andreas Schirmer, 2000. "Case‐based reasoning and improved adaptive search for project scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(3), pages 201-222, April.

    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:eee:ejores:v:303:y:2022:i:1:p:143-158. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.