New nurse scheduling problem considering burnout factor and undesirable shifts under COVID-19 (A real case study).

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

2 Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

3 Department of Mathematics Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran

4 Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran

Abstract

In recent years, the outbreak of COVID-19 has led to burnout of healthcare personnel. Accordingly, more attention should be paid to nurses scheduling and their preferences. The Nurse Scheduling Problem (NSP) as an optimization concept provides suitable nurses' schedules by focusing on the system requirements. In this study, a new NSP is developed in which the factors and consequences of nurses' burnout are considered simultaneously. In the proposed model, new constraints are formulated to define the undesirable shifts. Due to the seniority rules, it is tried to restrict the numeral of these shifts in the generated timetable to improve the burnout of nurses. In addition, an attempt is made to fairly allocate the requested leave of nurses by considering their leave days during the previous horizons. In the presented model, the timetable of nurses is flexible to cope with the absence of employees, and the required personnel are covered by changing shifts among nurses. To solve the developed problem, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are coded, and their results are compared with GAMS in different test problems. Taguchi method has been applied for parameter tuning of these algorithms. The final results prove that the GA outperforms PSO both in obtained solution and CPU time. GA has only a 0.22 % optimality gap on average. Finally, the proposed model is implemented in an actual case study in Iran. The generated timetable improves nurses' performance and the level of medical services by controlling the causes and consequences of burnout.

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Abdalkareem, Z. A., Amir, A., Al-Betar, M. A., Ekhan, P., & Hammouri, A. I. (2021). Healthcare scheduling in optimization context: a review. Health and Technology, 11, 445-469.
Abayomi-Alli, A. A., Uzedu, F. O., Misra, S., Abayomi-Alli, O. O., & Arogundade, O. T. (2022). Hybrid Model of Genetic Algorithms and Tabu Search Memory for Nurse Scheduling Systems. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 13(1), 1-20.
Amindoust, A., Asadpour, M., & Shirmohammadi, S. (2021). A hybrid genetic algorithm for nurse scheduling problem considering the fatigue factor. Journal of Healthcare Engineering, 2021.
Azaiez, M. N., & Al Sharif, S. S. (2005). A 0-1 goal programming model for nurse scheduling. Computers & Operations Research, 32(3), 491-507.
Azmoon, H., Nodooshan, H. S., Jalilian, H., Choobineh, A., & Shouroki, F. K. (2018). The relationship between fatigue and job burnout dimensions in hospital nurses. Health Scope, 7(2).
Bagheri, M., Devin, A. G., & Izanloo, A. (2016). An application of stochastic programming method for nurse scheduling problem in real word hospital. Computers & industrial engineering, 96, 192-200.
Benazzouz, T., Echchatbi, A., & Bellabdaoui, A. (2017). Planning problems of nurses: Case of a Moroccan healthcare unit. International Journal of Healthcare Management, 10(4), 243-251.
Ceschia, S., Di Gaspero, L., Mazzaracchio, V., Policante, G., & Schaerf, A. (2023). Solving a real-world nurse rostering problem by simulated annealing. Operations Research for Health Care, 36, 100379.
Cetin Yagmur, E., & Sarucan, A. (2019). Nurse scheduling with opposition-based parallel harmony search algorithm. Journal of Intelligent Systems, 28(4), 633-647.
Chen, Z., De Causmaecker, P., & Dou, Y. (2023). A combined mixed integer programming and deep neural network-assisted heuristics algorithm for the nurse rostering problem. Applied Soft Computing, 136, 109919.
El Adoly, A. A., Gheith, M., & Fors, M. N. (2018). A new formulation and solution for the nurse scheduling problem: A case study in Egypt. Alexandria engineering journal, 57(4), 2289-2298.
Gad, A. G. (2022). Particle swarm optimization algorithm and its applications: a systematic review. Archives of computational methods in engineering, 29(5), 2531-2561.
Güler, M. G., & Geçici, E. (2020). A decision support system for scheduling the shifts of physicians during COVID-19 pandemic. Computers & Industrial Engineering, 150, 106874.
Habibnejad-Ledari, H., Rabbani, M., & Ghorbani-Kutenaie, N. (2019). Solving a multi-objective model toward home care staff planning considering cross-training and staff preferences by NSGA-II and NRGA. Scientia Iranica, 26(5), 2919-2935.
Hamid, M., Tavakkoli-Moghaddam, R., Golpaygani, F., & Vahedi-Nouri, B. (2020). A multi-objective model for a nurse scheduling problem by emphasizing human factors. Proceedings of the institution of mechanical engineers, Part H: journal of engineering in medicine, 234(2), 179-199.
Hassani, M. R., & Behnamian, J. (2021). A scenario-based robust optimization with a pessimistic approach for nurse rostering problem. Journal of Combinatorial Optimization, 41, 143-169.
Holland, J. H. (1975). Adaptation in neural and artificial system. Ann Arbor, Univeristy of Michigan Press.
Ikeda, K., Nakamura, Y., & Humble, T. S. (2019). Application of quantum annealing to nurse scheduling problem. Scientific reports, 9(1), 12837.
Jafari, H., & Salmasi, N. (2015). Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. Journal of industrial engineering international, 11, 439-458.
Jafari, H., Bateni, S., Daneshvar, P., Bateni, S., & Mahdioun, H. (2016). Fuzzy mathematical modeling approach for the nurse scheduling problem: a case study. International journal of fuzzy systems, 18, 320-332.
Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia tools and applications, 80, 8091-8126.
Khalili, N., Shahnazari Shahrezaei, P., & Abri, A. G. (2020). A multi-objective optimization approach for a nurse scheduling problem considering the fatigue factor (case study: Labbafinejad Hospital). Journal of applied research on industrial engineering, 7(4), 396-423.
Kieft, R. A., de Brouwer, B. B., Francke, A. L., & Delnoij, D. M. (2014). How nurses and their work environment affect patient experiences of the quality of care: a qualitative study. BMC health services research, 14(1), 1-10.
Klyve, K. K., Senthooran, I., & Wallace, M. (2023). Nurse rostering with fatigue modelling: Incorporating a validated sleep model with biological variations in nurse rostering. Health Care Management Science, 26(1), 21-45.
Lee, H., Song, R., Cho, Y. S., Lee, G. Z., & Daly, B. (2003). A comprehensive model for predicting burnout in Korean nurses. Journal of advanced nursing, 44(5), 534-545.
Legrain, A., Omer, J., & Rosat, S. (2020). An online stochastic algorithm for a dynamic nurse scheduling problem. European Journal of Operational Research, 285(1), 196-210.
Lima, A., Moreira, M. T., Fernandes, C., Ferreira, M. S., Ferreira, M., Teixeira, J., ... & Coelho, A. (2023). The Burnout of Nurses in Intensive Care Units and the Impact of the SARS-CoV-2 Pandemic: A Scoping Review. Nursing Reports, 13(1), 230-242.
M’Hallah, R., & Alkhabbaz, A. (2013). Scheduling of nurses: A case study of a Kuwaiti health care unit. Operations Research for Health Care, 2(1-2), 1-19.
Muniyan, R., Ramalingam, R., Alshamrani, S. S., Gangodkar, D., Dumka, A., Singh, R., ... & Rashid, M. (2022). Artificial Bee Colony Algorithm with Nelder–Mead Method to Solve Nurse Scheduling Problem. Mathematics, 10(15), 2576.
Nasiri, M. M., & Rahvar, M. (2017). A two-step multi-objective mathematical model for nurse scheduling problem considering nurse preferences and consecutive shifts. International journal of services and operations management, 27(1), 83-101.
Nobil, A. H., Sharifnia, S. M. E., & Cárdenas-Barrón, L. E. (2022). Mixed integer linear programming problem for personnel multi-day shift scheduling: A case study in an Iran hospital. Alexandria Engineering Journal, 61(1), 419-426.
Rahimian, E., Akartunal─▒, K., & Levine, J. (2017). A hybrid integer and constraint programming approach to solve nurse rostering problems. Computers & Operations Research, 82, 83-94.
Ramli, M., Abas, Z., Ibrahim, N., & Hussin, B. (2016). Solving complex nurse scheduling problems using particle swarm optimization. International Review on Computers and Software (IRECOS), 11(8), 10.
Ramli, R., Abd Rahman, R., & Rohim, N. (2019). A hybrid ant colony optimization algorithm for solving a highly constrained nurse rostering problem. Journal of Information and Communication Technology, 18(3), 305-326.
Rerkjirattikal, P., Huynh, V. N., Olapiriyakul, S., & Supnithi, T. (2020). A goal programming approach to nurse scheduling with individual preference satisfaction. Mathematical Problems in Engineering, 2020, 1-11.
Sadeghilalimi, M., Mouhoub, M., & Said, A. B. (2023, May). Solving the Nurse Scheduling Problem Using the Whale Optimization Algorithm. In International Conference on Optimization and Learning (pp. 62-73). Cham: Springer Nature Switzerland.
Santos, H. G., Toffolo, T. A., Gomes, R. A., & Ribas, S. (2016). Integer programming techniques for the nurse rostering problem. Annals of Operations Research, 239, 225-251.
Saraswati, N. W. S., Artakusuma, I. D. M. D., & Indradewi, I. G. A. A. D. (2021, March). Modified genetic algorithm for employee work shifts scheduling optimization. In Journal of Physics: Conference Series (Vol. 1810, No. 1, p. 012014). IOP Publishing.
Soto, R., Crawford, B., Bertrand, R., & Monfroy, E. (2013). Modeling nrps with soft and reified constraints. AASRI Procedia, 4, 202-205.
Strandmark, P., Qu, Y., & Curtois, T. (2020). First-order linear programming in a column generation-based heuristic approach to the nurse rostering problem. Computers & Operations Research, 120, 104945.
Turhan, A. M., & Bilgen, B. (2020). A hybrid fix-and-optimize and simulated annealing approaches for nurse rostering problem. Computers & Industrial Engineering, 145, 106531.
Turhan, A. M., & Bilgen, B. (2022). A mat-heuristic based solution approach for an extended nurse rostering problem with skills and units. Socio-Economic Planning Sciences, 82, 101300.
Wang, D., Tan, D., & Liu, L. (2018). Particle swarm optimization algorithm: an overview. Soft computing, 22, 387-408.
Yilmaz, E. (2012). A mathematical programming model for scheduling of nurses’ labor shifts. Journal of medical systems, 36(2), 491-496.
Zhuang, Z. Y., & Vincent, F. Y. (2021). Analyzing the effects of the new labor law on outpatient nurse scheduling with law-fitting modeling and case studies. Expert Systems with Applications, 180, 115103.