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Annals of Operations Research

, Volume 272, Issue 1–2, pp 355–372 | Cite as

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

  • Adibah ShuibEmail author
  • Faiq Izzuddin Kamarudin
S.I. : Advances in Theoretical and Applied Combinatorial Optimization
  • 151 Downloads

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.

Keywords

Shift scheduling Mathematical programming Binary integer goal programming Days-off preference 

References

  1. Act 265 (2012). Employment (Amendment) Act 2012 (Malaysia, Act 1419) to the Employment Act 1955 (Malaysia, Act 265). http://minimumwages.mohr.gov.my/pdf/Act-265.pdf. Retrieved August 31, 2016.
  2. Aickelin, U., Burke, E., & Li, J. (2007). An estimation of distribution algorithm with intelligent local search for rule-based nurse rostering. Journal of the Operational Research Society, 58(12), 1574–1585.CrossRefGoogle Scholar
  3. Aickelin, U., & Dowsland, K. A. (2004). An indirect genetic algorithm for a nurse-scheduling problem. Computers & Operations Research, 31(5), 761–778.CrossRefGoogle Scholar
  4. Al-Yakoob, S. M., & Sherali, H. D. (2007). Mixed-integer programming models for an employee scheduling problem with multiple shifts and work locations. Annals of Operations Research, 155(1), 119–142.CrossRefGoogle Scholar
  5. Axellson, J., Åkerstedt, T., Kecklund, G., & Lowden, A. (2004). Tolerance to shift work—how does it relate to sleep and wakefulness? International Archives of Occupational and Environmental Health, 77(2), 121–129.CrossRefGoogle Scholar
  6. Azmat, C. S., Hürlimann, T., & Widmer, M. (2004). Mixed integer programming to schedule a single-shift workforce under annualized hours. Annals of Operations Research, 128(1–4), 199–215.CrossRefGoogle Scholar
  7. Bester, M. J., Nieuwoudt, I., & Van Vuuren, J. H. (2007). Finding good nurse duty schedules: A case study. Journal of Scheduling, 10(6), 387–405.CrossRefGoogle Scholar
  8. Bhulai, S., Koole, G., & Pot, A. (2008). Simple methods for shift scheduling in multiskill call centers. Manufacturing & Service Operations Management, 10(3), 411–420.CrossRefGoogle Scholar
  9. Bonutti, A., Ceschia, S., De Cesco, F., Musliu, N., & Schaerf, A. (2017). Modeling and solving a real-life multi-skill shift design problem. Annals of Operations Research, 252(2), 365–382.CrossRefGoogle Scholar
  10. Bruni, R., & Detti, P. (2014). A flexible discrete optimization approach to the physician scheduling problem. Operations Research for Health Care, 3(4), 191–199.CrossRefGoogle Scholar
  11. Brunner, J. O., Bard, J. F., & Kolisch, R. (2009). Flexible shift scheduling of physicians. Health Care Management Science, 12(3), 285–305.CrossRefGoogle Scholar
  12. Bunton, J., Ernst, A., & Krishnamoorthy, M. (2017). An integer programming based ant colony optimisation method for nurse rostering. Proceedings of the Federated Conference on Computer Science and Information Systems., 11, 407–414.CrossRefGoogle Scholar
  13. Celia, A. G., & Roger, A. K. (2010). The nurse rostering problem: A critical appraisal of the problem structure. European Journal of Operational Research, 202, 379–389.CrossRefGoogle Scholar
  14. Çetin, E., & Sarucan, A. (2015). Nurse scheduling using binary fuzzy goal programming. In Proceedings of the 2015 6th international conference on modeling, simulation, and applied optimization (ICMSAO), pp. 1–6. IEEE.Google Scholar
  15. Cuevas, R., Ferrer, J.-C., Klapp, M., & Muñoz, J.-C. (2016). A mixed integer programming approach to multi-skilled workforce scheduling. Journal of Scheduling, 19(1), 91–106.CrossRefGoogle Scholar
  16. Dahmen, S., & Rekik, M. (2015). Solving multi-activity multi-day shift scheduling problems with a hybrid heuristic. Journal of Scheduling, 18, 207–223.CrossRefGoogle Scholar
  17. Eitzen, G., Panton, D., & Mills, G. (2004). Multi-skilled workforce optimisation. Annals of Operations Research, 127(1–4), 359–372.CrossRefGoogle Scholar
  18. Elshafei, M., & Alfares, H. A. (2008). Dynamic programming algorithm for days-off scheduling with sequence dependent labor costs. Journal of Scheduling, 11(2), 85–93.CrossRefGoogle Scholar
  19. Lau, H. C. (1996). On the complexity of manpower shift scheduling. Computers & Operations Research, 23(1), 93–100.CrossRefGoogle Scholar
  20. Legrain, A., Bouarab, H., & Lahrichi, N. (2015). The nurse scheduling problem in real-life. Journal of Medical Systems, 39(1), 160.CrossRefGoogle Scholar
  21. Lin, C., Kang, J., Chiang, D., & Chen, C. (2015). Nurse scheduling with joint normalized shift and day-off preference satisfaction using a genetic algorithm with immigrant scheme. International Journal of Distributed Sensor Networks, 2015, 1–10.CrossRefGoogle Scholar
  22. Osogami, T., & Imai, H. (2000). Classification of various neighbourhood operations for the nurse. Scheduling Problem Lecture Notes in Computer Science, 1969, 72–83.CrossRefGoogle Scholar
  23. Quimper, C. G., & Rousseau, L. M. (2010). A large neighbourhood search approach to the multi-activity shift scheduling problem. Journal of Heuristics, 16, 373–392.CrossRefGoogle Scholar
  24. Talbi, E. G. (2009). Metaheuristics: From design to implementation. Wiley Series on Parallel and Distributed Computing: John Wiley & Sons Inc.CrossRefGoogle Scholar
  25. Todorovic, N., & Petrovic, S. (2013). Bee colony optimization algorithm for nurse rostering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(2), 467–473.CrossRefGoogle Scholar
  26. Todovic, D., Makajic-Nikolic, D., Kostic-Stankovic, M., & Martic, M. (2015). Police officer scheduling using goal programming. Policing: An International Journal of Police Strategies & Management, 38(2), 295–313.CrossRefGoogle Scholar
  27. Topaloglu, S. (2009). A shift scheduling model for employees with different seniority levels and an application in healthcare. European Journal of Operational Research, 198(3), 943–957.CrossRefGoogle Scholar
  28. Topaloglu, S., & Ozkarahan, I. (2004). An implicit goal programming model for the tour scheduling problem considering the employee work preferences. Annals of Operations Research, 128(1–4), 135–158.CrossRefGoogle Scholar
  29. van der Veen, E., Hans, E. W., Post, G. F., & Veltman, B. (2015). Shift rostering using decomposition: Assign weekend shifts first. Journal of Scheduling, 18(1), 29–43.CrossRefGoogle Scholar
  30. Wang, S. P., Hsieh, Y. K., Zhuang, Z. Y., & Ou, N. C. (2014). Solving an outpatient nurse scheduling problem by binary goal programming. Journal of Industrial and Production Engineering, 31(1), 41–50.CrossRefGoogle Scholar
  31. Wong, T. C., Xu, M., & Chin, K. S. (2014). A two-stage heuristic approach for nurse scheduling problem: A case study in an emergency department. Computers & Operations Research, 51, 99–110.CrossRefGoogle Scholar
  32. Wren, A. (1996). Scheduling, timetabling and rostering—A special relationship? Practice and Theory of Automated Timetabling, 1153, 46–75.CrossRefGoogle Scholar
  33. Xie, L., Merschformann, M., Kliewer, N., & Suhl, N. (2017). Metaheuristics approach for solving personalized crew rostering problem in public bus transit. Journal of Heuristics, 23(5), 321–347.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for Mathematics Studies, Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARAShah AlamMalaysia

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