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

Author

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.

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