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Bi-objective workforce-constrained maintenance scheduling: a case study

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  • N Safaei

    (University of Toronto)

  • D Banjevic

    (University of Toronto)

  • A K S Jardine

    (University of Toronto)

Abstract

In this paper, a real maintenance workforce-constrained scheduling problem is formulated as a bi-objective mixed-integer programming model with the aim of simultaneously minimizing the workforce requirements and maximizing the equipment availability. The skilled workforce is provided by internal and external resources using regular time, overtime and contracting. The equipment availability is measured by the downtime required for preventive maintenance (scheduled) and failure repair (unscheduled) jobs. We also encounter imminent or potential failures whose priorities depend on the severity of the failure on the system (secondary failure). The total weighted flow time is used as a scheduling criterion to measure the equipment availability; the weight of each job directly depends on the expected downtime resulting from the associated failure. The proposed model is verified using two comprehensive numerical examples and some sensitivity analyses. We conclude by discussing the results.

Suggested Citation

  • N Safaei & D Banjevic & A K S Jardine, 2011. "Bi-objective workforce-constrained maintenance scheduling: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1005-1018, June.
    • Handle: RePEc:pal:jorsoc:v:62:y:2011:i:6:d:10.1057_jors.2010.51
    DOI: 10.1057/jors.2010.51
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    References listed on IDEAS

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    1. R. C. Vergin, 1966. "Scheduling Maintenance and Determining Crew Size for Stochastically Failing Equipment," Management Science, INFORMS, vol. 13(2), pages 52-65, October.
    2. Przybylski, Anthony & Gandibleux, Xavier & Ehrgott, Matthias, 2008. "Two phase algorithms for the bi-objective assignment problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 509-533, March.
    3. Quan, Gang & Greenwood, Garrison W. & Liu, Donglin & Hu, Sharon, 2007. "Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1969-1984, March.
    4. John J. McCall, 1965. "Maintenance Policies for Stochastically Failing Equipment: A Survey," Management Science, INFORMS, vol. 11(5), pages 493-524, March.
    5. Matthijs C. Dijkstra & Leo G. Kroon & Marc Salomon & Jo A. E. E. van Nunen & Luk N. Van Wassenhove, 1994. "Planning the Size and Organization of KLM's Aircraft Maintenance Personnel," Interfaces, INFORMS, vol. 24(6), pages 47-58, December.
    6. Bérubé, Jean-François & Gendreau, Michel & Potvin, Jean-Yves, 2009. "An exact [epsilon]-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits," European Journal of Operational Research, Elsevier, vol. 194(1), pages 39-50, April.
    7. Christian Prins & Caroline Prodhon & Roberto Calvo, 2006. "Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem," Annals of Operations Research, Springer, vol. 147(1), pages 23-41, October.
    8. Harvey M. Wagner & Richard J. Giglio & R. George Glaser, 1964. "Preventive Maintenance Scheduling by Mathematical Programming," Management Science, INFORMS, vol. 10(2), pages 316-334, January.
    9. Mohan Gopalakrishnan & Sanjay L. Ahire & David M. Miller, 1997. "Maximizing the Effectiveness of a Preventive Maintenance System: An Adaptive Modeling Approach," Management Science, INFORMS, vol. 43(6), pages 827-840, June.
    10. Yang, Ta-Hui & Yan, Shangyao & Chen, Hsuan-Hung, 2003. "An airline maintenance manpower planning model with flexible strategies," Journal of Air Transport Management, Elsevier, vol. 9(4), pages 233-239.
    11. M Alardhi & A W Labib, 2008. "Preventive maintenance scheduling of multi-cogeneration plants using integer programming," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 503-509, April.
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    2. Hesham K. Alfares, 2022. "Plant shutdown maintenance workforce team assignment and job scheduling," Journal of Scheduling, Springer, vol. 25(3), pages 321-338, June.
    3. De Bruecker, Philippe & Beliën, Jeroen & Van den Bergh, Jorne & Demeulemeester, Erik, 2018. "A three-stage mixed integer programming approach for optimizing the skill mix and training schedules for aircraft maintenance," European Journal of Operational Research, Elsevier, vol. 267(2), pages 439-452.

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