IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v123y2003i1p203-22210.1023-a1026131531250.html
   My bibliography  Save this article

A Genetic Algorithm Based Approach for the Uncapacitated Continuous Location–Allocation Problem

Author

Listed:
  • S. Salhi
  • M.D.H. Gamal

Abstract

A GA-based approach is introduced to address the continuous location–allocation problem. Selection and removal procedures based on groups of chromosomes instead of individual chromosomes are put forward and specific crossover and mutation operators that rely on the impact of the genes are proposed. A new operator that injects once in a while new chromosomes into the population is also introduced. This provides diversity within the search and attempts to avoid early convergence. This approach is tested on existing data sets using several runs to evaluate the robustness of the proposed GA approach. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • S. Salhi & M.D.H. Gamal, 2003. "A Genetic Algorithm Based Approach for the Uncapacitated Continuous Location–Allocation Problem," Annals of Operations Research, Springer, vol. 123(1), pages 203-222, October.
    • Handle: RePEc:spr:annopr:v:123:y:2003:i:1:p:203-222:10.1023/a:1026131531250
    DOI: 10.1023/A:1026131531250
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/A:1026131531250
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/A:1026131531250?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zainuddin, Z.M. & Salhi, S., 2007. "A perturbation-based heuristic for the capacitated multisource Weber problem," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1194-1207, June.
    2. Chandra Irawan & Said Salhi, 2015. "Solving large $$p$$ p -median problems by a multistage hybrid approach using demand points aggregation and variable neighbourhood search," Journal of Global Optimization, Springer, vol. 63(3), pages 537-554, November.
    3. H Younies & G O Wesolowsky, 2007. "Planar maximal covering location problem under block norm distance measure," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(6), pages 740-750, June.
    4. Abdolsalam Ghaderi & Mohammad Jabalameli & Farnaz Barzinpour & Ragheb Rahmaniani, 2012. "An Efficient Hybrid Particle Swarm Optimization Algorithm for Solving the Uncapacitated Continuous Location-Allocation Problem," Networks and Spatial Economics, Springer, vol. 12(3), pages 421-439, September.
    5. M. Neema & K. Maniruzzaman & A. Ohgai, 2011. "New Genetic Algorithms Based Approaches to Continuous p-Median Problem," Networks and Spatial Economics, Springer, vol. 11(1), pages 83-99, March.
    6. Irawan, Chandra Ade & Salhi, Said & Scaparra, Maria Paola, 2014. "An adaptive multiphase approach for large unconditional and conditional p-median problems," European Journal of Operational Research, Elsevier, vol. 237(2), pages 590-605.
    7. Shiripour, Saber & Mahdavi-Amiri, Nezam, 2019. "Optimal distribution of the injured in a multi-type transportation network with damage-dependent travel times: Two metaheuristic approaches," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    8. Jean-Paul Arnaout & John Khoury, 2022. "Adaptation of WO to the Euclidean location-allocation with unknown number of facilities," Annals of Operations Research, Springer, vol. 315(1), pages 57-72, August.
    9. Emel Aktaş & Özay Özaydın & Burçin Bozkaya & Füsun Ülengin & Şule Önsel, 2013. "Optimizing Fire Station Locations for the Istanbul Metropolitan Municipality," Interfaces, INFORMS, vol. 43(3), pages 240-255, May-June.
    10. Yang, Lili & Jones, Bryan F. & Yang, Shuang-Hua, 2007. "A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 181(2), pages 903-915, September.

    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:spr:annopr:v:123:y:2003:i:1:p:203-222:10.1023/a:1026131531250. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.