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Staff Rostering Optimization: Ideal Recommendations vs. Real-World Computing Challenges

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Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 283))

Abstract

Staff rostering is a difficult and time-consuming problem that every company or institution that has employees working on shifts or on irregular working days must solve. The Finnish Institute of Occupational Health, which operates under the Ministry of Social Affairs and Health, published their recommendations for shift work in 2019. The recommended values for these individual factors are well justified. However, problems arise when all these recommendations should be satisfied together in real-world staff rostering. This paper shows what can be done to reach the best compromise considering the ideal recommendations, the employer’s point of view and the employees’ point of view. We use the PEAST metaheuristic, a computational intelligence framework, to show how the recommendations and employer requirements compete with each other. We give justification of why we can safely rely on the practical findings given by the metaheuristic.

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References

  1. L. Di Gaspero, J. Gärtner, N. Musliu, A. Schaerf, W. Schafhauser and W. Slany, “Automated Shift Design and Break Scheduling”, In: Uyar A., Ozcan E., Urquhart N. (eds) Automated Scheduling and Planning. Studies in Computational Intelligence 505, Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  2. Nurmi, K., Kyngäs, N., Kyngäs, J.: Workforce optimization: the general task-based shift generation problem. IAENG Int, J. Appl. Math. 49(4), 393–400 (2019)

    MathSciNet  Google Scholar 

  3. Van den Bergh, J., Belien, J., De Bruecker, P., Demeulemeester, E., De Boeck, L.: Personnel scheduling: a literature review. Eur. J. Oper. Res. 226(3), 367–385 (2013)

    Article  MathSciNet  Google Scholar 

  4. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and co., New York (1979)

    Google Scholar 

  5. Bartholdi, J.J.: A guaranteed-accuracy round-off algorithm for cyclic scheduling and set covering. Oper. Res. 29, 501–510 (1981)

    Google Scholar 

  6. Ernst, A.T., Jiang, H., Krishnamoorthy, M., Sier, D.: Staff scheduling and rostering: a review of applications, methods and models. Eur. J. Oper. Res. 153(1), 3–27 (2004)

    Article  MathSciNet  Google Scholar 

  7. Bard, J., Purnomo, H.: Hospital-wide reactive scheduling of nurses with preference considerations. IIE Trans. 37(7), 589–608 (2005)

    Article  Google Scholar 

  8. Burke, E., De Causmaecker, P., Petrovic, S., Vanden Berghe, G.: Metaheuristics for handling time interval coverage constraints in nurse scheduling. Appl. Artif. Intell. 20, 743–766 (2006)

    Google Scholar 

  9. Bilgin, B., De Causmaecker, P., Rossie, B., Vanden Berghe, G.: Local search neighbourhoods to deal with a novel nurse rostering model. In: Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling, Montréal, Canada (2008)

    Google Scholar 

  10. Beddoe, G.R., Petrovic, S., Li, J.: A hybrid metaheuristic case-based reasoning system for nurse rostering. J. Sched. 12, 99–119 (2009)

    Article  Google Scholar 

  11. Burke, E.K., Curtois, T.: New approaches to nurse rostering benchmark instances. Eur. J. Oper. Res. 237, 71–81 (2014)

    Article  MathSciNet  Google Scholar 

  12. Nurmi, K., Kyngäs, J., Kyngäs, N.: The PEAST algorithm - the key to optimizing workforce management and professional sports league schedules. Int. J. Process Manage. Benchmarking 4(4), 406–423 (2014)

    Article  Google Scholar 

  13. Jin, H., Post, G., van der Veen, E.: ORTEC’s contribution to the second international nurse rostering competition. In: Proceedings of the 11th International Conference on the Practice and Theory of Automated Timetabling, pp. 499–501 (2016)

    Google Scholar 

  14. Kingston, J.H.: KHE18: a solver for nurse rostering. In: Proceedings of the of the 12th International Conference on Practice and Theory of Automated Timetabling, pp. 113–127 (2018)

    Google Scholar 

  15. Gärtner, J., Bohle, P., Arlinghaus, A., Schafhauser, W., Krennwallner, T., Widl, M.: Scheduling matters - some potential requirements for future rostering competitions from a practitioner’s view. In: Proceedings of the 12th International Conference on Practice and Theory of Automated Timetabling, pp. 33–42 (2018)

    Google Scholar 

  16. The Finnish Institute of Occupational Health, “Recommendations for shift work. https://www.ttl.fi/tyontekija/tyoaika/tyoaikojen-kuormittavuuden-arviointi/tyoaikojenkuormittavuuden-arviointi-jaksotyossa/. Accessed 23 Oct 2020. (in Finnish)

  17. Arendt, J.: Shift work: coping with the biological clock. Occup. Med. 60(1), 10–20 (2010)

    Article  Google Scholar 

  18. Hinnenberg, S., Zegger, C., Nachreiner, F., Horn, D.: The utility of time - revisited after 25 years. Shiftwork Int. Newsletter 24(2) (2009)

    Google Scholar 

  19. Wirtz, A., Giebel, O., Schomann, C., Nachreiner, F.: The interference of flexible working times with the utility of time: a predictor of social impairment? Chronobiol. Int. 25, 249–261 (2008)

    Article  Google Scholar 

  20. Vedaa, Ø., et al.: Short rest between shifts (quick returns) and night work is associated with work-related accidents. Int. Arch. Occup. Environ. Health 92(6), 829–835 (2019). https://doi.org/10.1007/s00420-019-01421-8

    Article  Google Scholar 

  21. Nijp, H.H., Beckers, D.G., Geurts, S.A.: Systematic review on the association between employee worktime control and work-non-work balance, health and well-being, and job-related outcomes. Scand. J. Work Environ. Health 38, 299–313 (2012)

    Article  Google Scholar 

  22. Karhula, K., et al.: Are changes in objective working hour characteristics associated with changes in work-life conflict among hospital employees working shifts? A 7-year follow-up. Occup. Environ. Med. 75(6), 407–411 (2018)

    Article  Google Scholar 

  23. Karhula, K., Hakola, T., Koskinen, A., Ojajärvi, A., Kivimäki, M., Härmä, M.: Permanent night workers' sleep and psychosocial factors in hospital work. A comparison to day and shift work. Chronobiol. Int. 35(6), 785–794 (2018)

    Google Scholar 

  24. Sörensen, K., Glover, F.: Metaheuristics. In: Gass, S.I., Fu, M. (eds.) Encyclopedia of Operations Research and Management Science, vol. 62, pp. 960–970 (2013)

    Google Scholar 

  25. Sörensen, K., Sevaux, M., Glover, F.: A History of Metaheuristics. In: Martí, R., Pardalos, P.M., Resende, M.G.C. (eds.) Handbook of Heuristics, pp. 791–808. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-07124-4_4

    Chapter  Google Scholar 

  26. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  27. Glover, F.: Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res. 13(5), 533–549 (1986)

    Article  MathSciNet  Google Scholar 

  28. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, USA (1989)

    MATH  Google Scholar 

  29. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the First European Conference on Artificial Life, pp. 134–142 (1992)

    Google Scholar 

  30. Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)

    Article  MathSciNet  Google Scholar 

  31. Dees, W.A., Smith II, R.: Performance of interconnection rip-up and reroute strategies. In: 18th Design Automation Conference, pp. 382–390 (1981)

    Google Scholar 

  32. Schrimpf, G., Schneider, K., Stamm-Wilbrandt, H., Dueck, W.: Record breaking optimization results using the ruin and recreate principle. J. Comput. Phys. 159, 139–171 (2000)

    Article  MathSciNet  Google Scholar 

  33. Glover, F.: New ejection chain and alternating path methods for traveling salesman problems. Computer Science and Operations Research: New Developments in Their Interfaces, pp. 449–509 (1992)

    Google Scholar 

  34. Wolpert, D.H., Macready, W.G.: no free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997)

    Article  Google Scholar 

  35. Kyngäs, N., Nurmi, K., Kyngäs, J.: Crucial components of the PEAST algorithm in solving real-world scheduling problems. J. Lect. Not. Software Eng. 1(3), 230–236 (2013)

    Article  Google Scholar 

  36. Nurmi, K., Kyngäs, J., Järvelä, A.I.: Ten-year evolution and the experiments in scheduling a major ice hockey league. In: Hak, D. (ed.) An in Depth Guide to Sports, Nova Science Publishers, pp 169–207 (2018)

    Google Scholar 

  37. Kyngäs, J., Nurmi, K., Kyngäs, N., Lilley, G., Salter, T., Goossens, D.: Scheduling the Australian football league. J. Oper. Res. Soc. 68, 973–982 (2017)

    Article  Google Scholar 

  38. Kyngäs, N., Nurmi, K., Kyngäs, J.: Workforce scheduling using the PEAST algorithm. In: Ao, S.-I. (ed.) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol. 275, pp. 359–372. Springer, New York (2014)

    Chapter  Google Scholar 

  39. Kyngäs, N., Nurmi, K., Goossens, D.: The general task-based shift generation problem: formulation and benchmarks. In: Proceedings of the 9th Multidisciplinary Int. Scheduling Conference: Theory and Applications (MISTA), Ningbo, China (2019)

    Google Scholar 

  40. Kyngäs, N., Nurmi, K., Kyngäs, J.: Solving the person-based multitask shift generation problem with breaks. In: Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization, Hammamet, Tunis, pp. 1–8 (2013)

    Google Scholar 

  41. Nurmi, K., Kyngäs, J.: A conversion scheme for turning a curriculum-based timetabling problem into a school timetabling problem. In: Proceedings of the 7th Conference on the Practice and Theory of Automated Timetabling (PATAT), Montreal, Canada (2008)

    Google Scholar 

  42. Nurmi, K., Goossens, D., Kyngäs, J.: Scheduling a triple round robin tournament with minitournaments for the Finnish national youth ice hockey league. J. Oper. Res. Soc. 65(11), 1770–1779 (2014)

    Article  Google Scholar 

  43. Nurmi, K., Kyngäs, J.: Days-off scheduling for a bus transportation company. Int. J. Innovative Comput. Appl. 3(1), 42–49 (2011)

    Article  Google Scholar 

  44. Nurmi, K., et al.: A framework for scheduling professional sports leagues. In: Ao, S.-I. (ed.) IAENG Transactions on Engineering Technologies, vol. 5, pp. 14–28. Springer, Heidelberg (2010)

    Google Scholar 

  45. Nurmi, K., Kyngäs, J., Kyngäs, N.: Synthesis of employer and employee satisfaction - case nurse rostering in a Finnish hospital. J. Adv. Inf. Technol. 7(2), 97–104 (2016)

    Google Scholar 

  46. Nurmi, K., Kyngäs, J., Kyngäs, N.: The core staff rostering problem. In: Ao, C., Katagiri and Xu (eds.) IAENG Transactions on Engineering Sciences - Special Issue for the International Association of Engineers Conferences, World Scientific (2016)

    Google Scholar 

  47. Kyngäs, N., Nurmi, K., Ásgeirsson, E.I., Kyngäs, J.: Using the PEAST algorithm to roster nurses in an intensive-care unit in a Finnish hospital. In: Proceedings of the 9th Conference on the Practice and Theory of Automated Timetabling, pp. 83–93 (2012)

    Google Scholar 

  48. Finnish Institute of Occupational Health: Working hours, health, well-being and participation in working life – WOW (2015–2020). https://www.ttl.fi/en/research-and-development-projects/wow/. Accessed 20 Feb 2020

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Authors and Affiliations

  1. Satakunta University of Applied Sciences, Satakunnankatu 23, 28130, Pori, Finland

    Kimmo Nurmi, Jari Kyngäs & Nico Kyngäs

Authors
  1. Kimmo Nurmi
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  2. Jari Kyngäs
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  3. Nico Kyngäs
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Correspondence to Kimmo Nurmi .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Nurmi, K., Kyngäs, J., Kyngäs, N. (2022). Staff Rostering Optimization: Ideal Recommendations vs. Real-World Computing Challenges. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_15

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