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Mixed-integer programming models for an employee scheduling problem with multiple shifts and work locations

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  • Salem Al-Yakoob
  • Hanif Sherali

Abstract

This paper is concerned with the problem of assigning employees to gas stations owned by the Kuwait National Petroleum Corporation (KNPC), which hires a firm to prepare schedules for assigning employees to about 86 stations distributed all over Kuwait. Although similar employee scheduling problems have been addressed in the literature, certain peculiarities of the problem require novel mathematical models and algorithms to deal with the specific nature and size of this problem. The problem is modeled as a mixed-integer program, and a problem size analysis based on real data reveals that the formulation is too complex to solve directly. Hence, a two-stage approach is proposed, where the first stage assigns employees to stations, and the second stage specifies shifts and off-days for each employee. Computational results related to solving the two-stage models directly via CPLEX and by specialized heuristics are reported. The two-stage approach provides daily schedules for employees for a given time horizon in a timely fashion, taking into consideration the employees’ expressed preferences. This proposed modeling approach can be incorporated within a decision support system to replace the current manual scheduling practice that is often chaotic and has led to feelings of bias and job dissatisfaction among employees. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:155:y:2007:i:1:p:119-142:10.1007/s10479-007-0210-4
    DOI: 10.1007/s10479-007-0210-4
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    References listed on IDEAS

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    Cited by:

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    2. Douglas S. Altner & Erica K. Mason & Les D. Servi, 2019. "Two-stage stochastic days-off scheduling of multi-skilled analysts with training options," Journal of Combinatorial Optimization, Springer, vol. 38(1), pages 111-129, July.
    3. Van den Bergh, Jorne & Beliën, Jeroen & De Bruecker, Philippe & Demeulemeester, Erik & De Boeck, Liesje, 2013. "Personnel scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 226(3), pages 367-385.
    4. Peña Diana A. & Osorio Andrés F. & Orejuela Juan P. & Idarraga Juan C., 2023. "Multi-skilled workforce scheduling with training and welfare considerations," Engineering Management in Production and Services, Sciendo, vol. 15(3), pages 27-41, September.
    5. Douglas S. Altner & Anthony C. Rojas & Leslie D. Servi, 2018. "A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options," Journal of Scheduling, Springer, vol. 21(5), pages 517-531, October.
    6. 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.
    7. Tom Rihm & Philipp Baumann, 2018. "Staff assignment with lexicographically ordered acceptance levels," Journal of Scheduling, Springer, vol. 21(2), pages 167-189, April.
    8. Emir Hüseyin Özder & Evrencan Özcan & Tamer Eren, 2019. "Staff Task-Based Shift Scheduling Solution with an ANP and Goal Programming Method in a Natural Gas Combined Cycle Power Plant," Mathematics, MDPI, vol. 7(2), pages 1-26, February.

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