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A variable neighbourhood search algorithm for the constrained task allocation problem

Author

Listed:
  • A Lusa

    (Universitat Politècnica de Catalunya (UPC))

  • C N Potts

    (University of Southampton)

Abstract

A variable neighbourhood search algorithm that employs new neighbourhoods is proposed for solving a task allocation problem whose main characteristics are: (i) each task requires a certain amount of resources and each processor has a capacity constraint which limits the total resource of the tasks that are assigned to it; (ii) the cost of solution includes fixed costs when using processors, task assignment costs, and communication costs between tasks assigned to different processors. A computational study shows that the algorithm performs well in terms of time and solution quality relative to other local search procedures that have been proposed.

Suggested Citation

  • A Lusa & C N Potts, 2008. "A variable neighbourhood search algorithm for the constrained task allocation problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(6), pages 812-822, June.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:6:d:10.1057_palgrave.jors.2602413
    DOI: 10.1057/palgrave.jors.2602413
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    References listed on IDEAS

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    1. Hamam, Yskandar & Hindi, Khalil S., 2000. "Assignment of program modules to processors: A simulated annealing approach," European Journal of Operational Research, Elsevier, vol. 122(2), pages 509-513, April.
    2. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    3. Andreas Ernst & Houyuan Jiang & Mohan Krishnamoorthy, 2006. "Exact Solutions to Task Allocation Problems," Management Science, INFORMS, vol. 52(10), pages 1634-1646, October.
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    Cited by:

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    2. Manuel Laguna & Francisco Gortázar & Micael Gallego & Abraham Duarte & Rafael Martí, 2014. "A black-box scatter search for optimization problems with integer variables," Journal of Global Optimization, Springer, vol. 58(3), pages 497-516, March.
    3. Lamb, John D., 2012. "Variable neighbourhood structures for cycle location problems," European Journal of Operational Research, Elsevier, vol. 223(1), pages 15-26.
    4. Michele Samorani & Manuel Laguna, 2012. "Data-Mining-Driven Neighborhood Search," INFORMS Journal on Computing, INFORMS, vol. 24(2), pages 210-227, May.
    5. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.

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