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Heuristics Based on Partial Enumeration for the Unrelated Parallel Processor Scheduling Problem

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  • E. Mokotoff
  • J.L. Jimeno

Abstract

The classical deterministic scheduling problem of minimizing the makespan on unrelated parallel processors is known to be NP-hard in the strong sense. Given the mixed integer linear model with binary decision variables, this paper presents heuristic algorithms based on partial enumeration. Basically, they consist in the construction of mixed integer subproblems, considering the integrality of some subset of variables, formulated using the information obtained from the solution of the linear relaxed problem. Computational experiments are reported for a collection of test problems, showing that some of the proposed algorithms achieve better solutions than other relevant approximation algorithms published up to now. Copyright Kluwer Academic Publishers 2002

Suggested Citation

  • E. Mokotoff & J.L. Jimeno, 2002. "Heuristics Based on Partial Enumeration for the Unrelated Parallel Processor Scheduling Problem," Annals of Operations Research, Springer, vol. 117(1), pages 133-150, November.
    • Handle: RePEc:spr:annopr:v:117:y:2002:i:1:p:133-150:10.1023/a:1021569406280
    DOI: 10.1023/A:1021569406280
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    Citations

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    Cited by:

    1. Fanjul-Peyro, Luis & Perea, Federico & Ruiz, Rubén, 2017. "Models and matheuristics for the unrelated parallel machine scheduling problem with additional resources," European Journal of Operational Research, Elsevier, vol. 260(2), pages 482-493.
    2. A Volgenant & I Y Zwiers, 2007. "Partial enumeration in heuristics for some combinatorial optimization problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 73-79, January.
    3. Zhi Pei & Mingzhong Wan & Ziteng Wang, 2020. "A new approximation algorithm for unrelated parallel machine scheduling with release dates," Annals of Operations Research, Springer, vol. 285(1), pages 397-425, February.
    4. Fanjul-Peyro, Luis & Ruiz, Rubén, 2010. "Iterated greedy local search methods for unrelated parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 207(1), pages 55-69, November.
    5. Anzanello, Michel J. & Fogliatto, Flavio S. & Santos, Luana, 2014. "Learning dependent job scheduling in mass customized scenarios considering ergonomic factors," International Journal of Production Economics, Elsevier, vol. 154(C), pages 136-145.

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