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Solving shift scheduling problem with days-off preference for power station workers using binary integer goal programming model

Author

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  • Adibah Shuib

    (Universiti Teknologi MARA)

  • Faiq Izzuddin Kamarudin

    (Universiti Teknologi MARA)

Abstract

Shift scheduling problem (SSP) is a complex NP-hard integer programming problem, especially when many shifts and large number of workers of various ranks and multi-skills are involved. Shift scheduling also needs to comply with certain labour regulations and organisation’s rules and policies. Various models for SSP have been developed and solved. However, studies on SSP for workers of power stations or utility providers are still lacking. This paper presents the study on shift scheduling of workers at the largest power station in Malaysia. The objectives of this study are to identify main criteria and conditions of SSP at the power station, to formulate a Binary Integer Goal Programming (BGP) model for SSP that optimises workers’ day-off preference and to determine the optimal schedule for workers based on the model. The scheduling focuses on three processes which are demand modelling, shift scheduling and day-off scheduling. The study involves scheduling 43 workers in a selected department of the power station for 28 days where workers work in three shifts (morning, evening and night shifts) and have standby and rest days. The SSP-BGP model considers workers’ day-off preference as its main feature and introduces objective function that maximises the overachievement and minimises under-achievement of the day-off preference. It addresses conflicting multi-objectives in determining the optimal schedule using the goal programming approach. The model also put forward new hard and soft constraints, which reflect the nature of shifts’ requirements for these workers when determining the optimal schedule. The required data have been obtained from the power station and validation and verification of the model has been acquired with the cooperation from its personnel. The SSP-BGP model was solved using MATLAB in reasonable time. It significantly reduces the time to obtain a monthly schedule as compared to current manually done scheduling. Based on the 28-day schedule obtained, the day-off preference’s satisfaction of workers has increased by 37.21% that is from 43.02% based on the existing prepared schedule to 80.23% according to the schedule produced based on the SSP-BGP model. The model has shown good performance by not only generating optimal schedule based on the required number and composition of workers for shifts but also providing positive impacts on workers through day-off preferences’ satisfaction and the possibility of working with different groups.

Suggested Citation

  • Adibah Shuib & Faiq Izzuddin Kamarudin, 2019. "Solving shift scheduling problem with days-off preference for power station workers using binary integer goal programming model," Annals of Operations Research, Springer, vol. 272(1), pages 355-372, January.
    • Handle: RePEc:spr:annopr:v:272:y:2019:i:1:d:10.1007_s10479-018-2848-5
    DOI: 10.1007/s10479-018-2848-5
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    References listed on IDEAS

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    1. Guy Eitzen & David Panton & Graham Mills, 2004. "Multi-Skilled Workforce Optimisation," Annals of Operations Research, Springer, vol. 127(1), pages 359-372, March.
    2. U Aickelin & E K Burke & J Li, 2007. "An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1574-1585, December.
    3. Seyda Topaloglu & Irem Ozkarahan, 2004. "An Implicit Goal Programming Model for the Tour Scheduling Problem Considering the Employee Work Preferences," Annals of Operations Research, Springer, vol. 128(1), pages 135-158, April.
    4. Carlos Azmat & Tony Hürlimann & Marino Widmer, 2004. "Mixed Integer Programming to Schedule a Single-Shift Workforce under Annualized Hours," Annals of Operations Research, Springer, vol. 128(1), pages 199-215, April.
    5. Alex Bonutti & Sara Ceschia & Fabio De Cesco & Nysret Musliu & Andrea Schaerf, 2017. "Modeling and solving a real-life multi-skill shift design problem," Annals of Operations Research, Springer, vol. 252(2), pages 365-382, May.
    6. Jens Brunner & Jonathan Bard & Rainer Kolisch, 2009. "Flexible shift scheduling of physicians," Health Care Management Science, Springer, vol. 12(3), pages 285-305, September.
    7. Sandjai Bhulai & Ger Koole & Auke Pot, 2008. "Simple Methods for Shift Scheduling in Multiskill Call Centers," Manufacturing & Service Operations Management, INFORMS, vol. 10(3), pages 411-420, December.
    8. Topaloglu, Seyda, 2009. "A shift scheduling model for employees with different seniority levels and an application in healthcare," European Journal of Operational Research, Elsevier, vol. 198(3), pages 943-957, November.
    9. Lin Xie & Marius Merschformann & Natalia Kliewer & Leena Suhl, 2017. "Metaheuristics approach for solving personalized crew rostering problem in public bus transit," Journal of Heuristics, Springer, vol. 23(5), pages 321-347, October.
    10. Salem Al-Yakoob & Hanif Sherali, 2007. "Mixed-integer programming models for an employee scheduling problem with multiple shifts and work locations," Annals of Operations Research, Springer, vol. 155(1), pages 119-142, November.
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    3. Mustafa Oğuz Afacan & Umut Dur & William Harris, 2021. "School Choice with Hybrid Schedules," Games, MDPI, vol. 12(2), pages 1-11, April.

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