A Decision Support System For Skill-Based Nurse Scheduling In An Intensive Care Unit

Year 2024, Volume: 37 Issue: 1, 284 - 308, 01.03.2024

Orhan Parıldar Çağdaş Erkan Akyürek Diyar Akay

https://doi.org/10.35378/gujs.1218158

Abstract

The main target of health institutions is to provide the health services needed by society at the desired quality with the lowest possible cost. Considering the total number of employees in health institutions, nurse assignment and scheduling have an essential role in increasing efficiency and improving service quality due to the one-to-one interaction of nurses with patients. This study proposes a nurse scheduling model based on nurses’ skill levels incorporated into a decision support system. The skill level of nurses is assessed using Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution method based on eight criteria. The nurse scheduling problem is then modeled with 0-1 Goal Programming, considering the skill assessment as a constraint. The practicality of the proposed model is examined for the assignment and scheduling conditions of nurses at the 3rd level of surgical intensive care in a general hospital, and the valuable aspects of the proposed approach are discussed. When the proposed solution is compared with the current situation, it is realized that one nurse is saved without worsening the constraints.

Keywords

Nurse Scheduling, Goal Programming, AHP, TOPSIS, Holt-Winter

References

• [1] Hooshangi-Tabrizi, P., Contreras, I., Bhuiyan, N., and Batist, G., “Improving patient-care services at an oncology clinic using a flexible and adaptive scheduling procedure”, Expert Systems with Applications, 150: 113267, (2020).
• [2] Jafari, H., Bateni, S., Daneshvar, P., Bateni, S., and Mahdioun, H., “Fuzzy mathematical modeling approach for the nurse scheduling problem: A case study”, International Journal of Fuzzy Systems, 18(2): 320-332, (2016).
• [3] Mullinax, C., and Lawley, M., “Assigning patients to nurses in neonatal intensive care”, Journal of the Operational Research Society, 53(1): 25-35, (2002).
• [4] Welton, J. M., Fischer, M. H., DeGrace, S., and Zone-Smith, L., “Hospital nursing costs, billing, and reimbursement”, Nursing Economics, 24(5): 239, (2006).
• [5] Maenhout, B., and Vanhoucke, M., “An integrated nurse staffing and scheduling analysis for longer-term nursing staff allocation problems”, Omega, 41(2): 485-499, (2013).
• [6] Legrain, A., Omer, J., and Rosat, S., “A rotation-based branch-and-price approach for the nurse scheduling problem”, Mathematical Programming Computation, 12(3): 417-450, (2020).
• [7] Lin, R. C., Sir, M. Y., Sisikoglu, E., Pasupathy, K., and Steege, L. M., “Optimal nurse scheduling based on quantitative models of work-related fatigue”, IIE Transactions on Healthcare Systems Engineering, 3(1): 23-38, (2013).
• [8] Mobasher, A., Lim, G., Bard, J. F., and Jordan, V., “Daily scheduling of nurses in operating suites”, IIE Transactions on Healthcare Systems Engineering, 1(4): 232-246, (2011).
• [9] Schoenfelder, J., Bretthauer, K. M., Wright, P. D., and Coe, E., “Nurse scheduling with quick-response methods: Improving hospital performance, nurse workload, and patient experience”, European Journal of Operational Research, 283(1): 390-403, (2020).
• [10] Fügener, A., Pahr, A., and Brunner, J. O., “Mid-term nurse rostering considering cross-training effects”, International Journal of Production Economics, 196: 176-187, (2018).
• [11] Gür, Ş., and Eren, T., “Scheduling and planning in service systems with goal programming: Literature review”, Mathematics, 6(11): 265, (2018).
• [12] Van den Bergh, J., Beliën, J., De Bruecker, P., Demeulemeester, E., and De Boeck, L., “Personnel scheduling: A literature review”, European Journal of Operational Research, 226(3): 367-385, (2013).
• [13] Ngoo, C. M., Goh, S. L., Sabar, N. R., Abdullah, S., and Kendall, G., “A survey of the nurse rostering solution methodologies: The state-of-the-art and emerging trends”, IEEE Access, (2022).
• [14] Al-Hinai, N., Al-Yazidy, N., Al-Hooti, A., and Al-Shereiqi, E., “A goal programming model for nurse scheduling at emergency department”, Proceedings of the International Conference on Industrial Engineering and Operations Management, 99-103, (2018).
• [15] Legrain, A., Omer, J., and Rosat, S., “An online stochastic algorithm for a dynamic nurse scheduling problem”, European Journal of Operational Research, 285(1): 196-210, (2020).
• [16] Liu, Q., Niu, B., Wang, J., Wang, H. and Li, L., “Nurse scheduling problem based on hydrologic cycle optimization”. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 1398-1405, (2019).
• [17] Thielen, C., “Duty rostering for physicians at a department of orthopedics and trauma surgery”, Operations Research for Health Care, 19: 80-91, (2018).
• [18] Carello, G., Lanzarone, E., and Mattia, S., “Trade-off between stakeholders’ goals in the home care nurse-to-patient assignment problem”, Operations Research for Health Care, 16: 29-40, (2018).
• [19] Azaiez, M. N., and Al Sharif, S. S., “A 0-1 goal programming model for nurse scheduling”, Computers & Operations Research, 32(3): 491-507, (2005).
• [20] El Adoly, A. A., Gheith, M., and Nashat Fors, M., “A new formulation and solution for the nurse scheduling problem: A case study in egypt”, Alexandria Engineering Journal, 57(4): 2289-2298, (2018).
• [21] Ahmed, A., and Ali, H., “Modeling patient preference in an operating room scheduling problem”, Operations Research for Health Care, 25: 100257, (2020).
• [22] Kullberg, A., Bergenmar, M., and Sharp, L., “Changed nursing scheduling for improved safety culture and working conditions - patients' and nurses' perspectives”, Journal of Nursing Management, 24(4): 524-532, (2016).
• [23] Burke, E. K., De Causmaecker, P., Berghe, G. V., and Van Landeghem, H., “The state of the art of nurse rostering”, Journal of Scheduling, 7(6): 441-499, (2004).
• [24] Winston, W. L., and Goldberg, J. B., “Operations research: applications and algorithms” Thomson Brooks/Cole Belmont, (2004).
• [25] Jantan, H., Hamdan, A. R., and Othman, Z. A., “Knowledge discovery techniques for talent forecasting in human resource application”, World Academy of Science, Engineering and Technology, 50: 775-783, (2009).
• [26] Hughes, J. C., and Rog, E., “Talent management: A strategy for improving employee recruitment, retention and engagement within hospitality organizations”, International Journal of Contemporary Hospitality Management, (2008).
• [27] D'Annunzio‐Green, N., Maxwell, G., Watson, S., Scott, B., and Revis, S., “Talent management in hospitality: Graduate career success and strategies”, International Journal of Contemporary Hospitality Management, (2008).
• [28] Lim, G. J., Mobasher, A., Bard, J. F., and Najjarbashi, A., “Nurse scheduling with lunch break assignments in operating suites”, Operations Research for Health Care, 10: 35-48, (2016).
• [29] Zhou, J., Fan, Y. and Zeng, H., “A nurse scheduling approach based on set pair analysis”, International Journal of Industrial Engineering: Theory Applications and Practice, 19(9): 359-368, (2012).
• [30] Awadallah, M. A., Khader, A. T., Al-Betar, M. A., and Bolaji, A. L., “Nurse scheduling using harmony search”. Proceedings - 2011 6th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2011, 58-63, (2011).
• [31] Topaloglu, S., and Selim, H., “Nurse scheduling using fuzzy modeling approach”, Fuzzy Sets and Systems, 161(11): 1543-1563, (2010).
• [32] Wong, T. C., Xu, M., and Chin, K. S., “A two-stage heuristic approach for nurse scheduling problem: A case study in an emergency department”, Computers and Operations Research, 51: 99-110, (2014).
• [33] Trilling, L., Guinet, A., and Le Magny, D., “Nurse scheduling using integer linear programming and constraint programming”. IFAC Proceedings Volumes (IFAC-PapersOnline), (2006).
• [34] Jenal, R., Ismail, W. R., Yeun, L. C., and Oughalime, A., “A cyclical nurse schedule using goal programming”, Journal of Mathematical and Fundamental Sciences, 43(3): 151-64, (2011).
• [35] Rosenbloom, E., and Goertzen, N., “Cyclic nurse scheduling”, European journal of operational research, 31(1): 19-23, (1987).
• [36] Millar, H. H., and Kiragu, M., “Cyclic and non-cyclic scheduling of 12 h shift nurses by network programming”, European journal of operational research, 104(3): 582-592, (1998).
• [37] Aickelin, U., Burke, E. K., and Li, J., “An evolutionary squeaky wheel optimization approach to personnel scheduling”, IEEE Transactions on Evolutionary Computation, 13(2): 433-443, (2009).
• [38] Jaumard, B., Semet, F., and Vovor, T., “A generalized linear programming model for nurse scheduling”, European Journal of Operational Research, 107(1): 1-18, (1998).
• [39] Isken, M. W., “An implicit tour scheduling model with applications in healthcare”, Annals of Operations Research, 128(1): 91-109, (2004).
• [40] Moz, M., and Pato, M. V., “Solving the problem of rerostering nurse schedules with hard constraints: New multicommodity flow models”, Annals of Operations Research, 128(1-4): 179-197, (2004).
• [41] Miller, H. E., Pierskalla, W. P., and Rath, G. J., “Nurse scheduling using mathematical programming”, Operations Research, 24(5): 857-870, (1976).
• [42] Warner, D. M., and Prawda, J., “A mathematical programming model for scheduling nursing personnel in a hospital”, Management Science, 19(4-part-1): 411-422, (1972).
• [43] Warner, M., Keller, B., and Martel, S., “Automated nurse scheduling”, Journal of the Society for Health Systems, 2(2): 66-80, (1991).
• [44] Bard, J. F., and Purnomo, H. W., “Short-term nurse scheduling in response to daily fluctuations in supply and demand”, Health Care Management Science, 8(4): 315-324, (2005).
• [45] Hasegawa, S., and Kosugi, Y., “Solving nurse scheduling problem by integer-programming-based local search”. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 1474-1480, (2006).
• [46] Rönnberg, E., and Larsson, T., “Automating the self-scheduling process of nurses in swedish healthcare: A pilot study”, Health Care Management Science, 13(1): 35-53, (2010).
• [47] Guo, H., Tang, J., and Qu, G., “Historical data-driven nurse flexible scheduling problem”. 2013 25th Chinese Control and Decision Conference, CCDC 2013, 1275-1280, (2013).
• [48] Maenhout, B., and Vanhoucke, M., “Analyzing the nursing organizational structure and process from a scheduling perspective”, Health Care Management Science, 16(3): 177-196, (2013).
• [49] Wright, P. D., and Mahar, S., “Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction”, Omega (United Kingdom), 41(6): 1042-1052, (2013).
• [50] Lin, C. C., Kang, J. R., Liu, W. Y., and Deng, D. J., “Modelling a nurse shift schedule with multiple preference ranks for shifts and days-off”, Mathematical Problems in Engineering, (2014).
• [51] Arthur, J. L., and Ravindran, A., “A multiple objective nurse scheduling model”, AIIE Transactions, 13(1): 55-60, (1981).
• [52] Burke, E. K., De Causmaecker, P., Petrovic, S., and Berghe, G. V., “A multi criteria meta-heuristic approach to nurse rostering”. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600), 1197-1202, (2002).
• [53] Chen, J. G., and Yeung, T. W., “Hybrid expert-system approach to nurse scheduling”, Computers in Nursing, 11(4): 183-190, (1993).
• [54] Berrada, I., Ferland, J. A., and Michelon, P., “A multi-objective approach to nurse scheduling with both hard and soft constraints”, Socio-economic Planning Sciences, 30(3): 183-193, (1996).
• [55] Jaszkiewicz, A., “A metaheuristic approach to multiple objective nurse scheduling”, Foundations of Computing and Decision Sciences, 22(3): 169-184, (1997).
• [56] Ozkarahan, I., “A disaggregation model of a flexible nurse scheduling support system”, Socio-Economic Planning Sciences, 25(1): 9-26, (1991).
• [57] Topaloglu, S., and Ozkarahan, I., “An implicit goal programming model for the tour scheduling problem considering the employee work preferences”, Annals of Operations Research, 128(1-4): 135-158, (2004).
• [58] Wang, S. P., Hsieh, Y. K., Zhuang, Z. Y., and Ou, N. C., “Solving an outpatient nurse scheduling problem by binary goal programming”, Journal of Industrial and Production Engineering, 31(1): 41-50, (2014).
• [59] Chen, P. S., Lin, Y. J., and Peng, N. C., “A two-stage method to determine the allocation and scheduling of medical staff in uncertain environments”, Computers and Industrial Engineering, 99: 174-188, (2016).
• [60] Abernathy, W. J., Baloff, N., Hershey, J. C., and Wandel, S., “A three-stage manpower planning and scheduling model—a service-sector example”, Operations Research, 21(3): 693-711, (1973).
• [61] Bagheri, M., Gholinejad Devin, A., and Izanloo, A., “An application of stochastic programming method for nurse scheduling problem in real word hospital”, Computers and Industrial Engineering, 96: 192-200, (2016).
• [62] Warner, D. M., “Scheduling nursing personnel according to nursing preference: A mathematical programming approach”, Operations Research, 24(5): 842-856, (1976).
• [63] Santos, D., Fernandes, P., Cardoso, H. L., and Oliveira, E., “A weighted constraint optimization approach to the nurse scheduling problem”. IEEE 18th International Conference on Computational Science and Engineering, 233-239, (2015).
• [64] He, F., and Qu, R., “A constraint programming based column generation approach to nurse rostering problems”, Computers & Operations Research, 39(12): 3331-3343, (2012).
• [65] Burke, E. K., and Curtois, T., “New approaches to nurse rostering benchmark instances”, European Journal of Operational Research, 237(1): 71-81, (2014).
• [66] Strandmark, P., Qu, Y. and Curtois, T., “First-order linear programming in a column generation-based heuristic approach to the nurse rostering problem”, Computers & Operations Research, 120: 104-945, (2020).
• [67] Smet, P., Brucker, P., De Causmaecker, P., and Berghe, G. V., “Polynomially solvable personnel rostering problems”, European Journal of Operational Research, 249(1): 67-75, (2016).
• [68] Andrew, A. M., “Modern heuristic search methods”, Kybernetes, 27(5): 582-585, (1998).
• [69] Inoue, T., Furuhashi, T., Fujii, M., Maeda, H., and Takaba, M., “Development of nurse scheduling support system using interactive ea”. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, V-533 - V-537, (1999).
• [70] Jan, A., Yamamoto, M., and Ohuchi, A., “Evolutionary algorithms for nurse scheduling problem”. Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000, 196-203, (2000).
• [71] Kawanaka, H., Yamamoto, K., Yoshikawa, T., Shinogi, T., and Tsuruoka, S., “Genetic algorithm with the constraints for nurse scheduling problem”. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC, 1123-1130, (2001).
• [72] Burke, E., Cowling, P., De Causmaecker, P., and Berghe, G. V., “A memetic approach to the nurse rostering problem”, Applied intelligence, 15(3): 199-214, (2001).
• [73] Aickelin, U., and Dowsland, K. A., “An indirect genetic algorithm for a nurse-scheduling problem”, Computers & Operations Research, 31(5): 761-778, (2004). [74] Ikegami, A., and Niwa, A., “A subproblem-centric model and approach to the nurse scheduling problem”, Mathematical Programming, Series B, 97(3): 517-541, (2003).
• [75] Bellanti, F., Carello, G., Della Croce, F., and Tadei, R., “A greedy-based neighborhood search approach to a nurse rostering problem”, European Journal of Operational Research, 153(1): 28-40, (2004).
• [76] Dowsland, K. A., “Nurse scheduling with tabu search and strategic oscillation”, European Journal of Operational Research, 106(2-3): 393-407, (1998).
• [77] Dowsland, K. A., and Thompson, J. M., “Solving a nurse scheduling problem with knapsacks, networks and tabu search”, Journal of the Operational Research Society, 51(7): 825-833, (2000).
• [78] Bard, J. F., and Purnomo, H. W., “A column generation-based approach to solve the preference scheduling problem for nurses with downgrading”, Socio-Economic Planning Sciences, 39(3): 193-213, (2005).
• [79] Bard, J. F., and Purnomo, H. W., “Preference scheduling for nurses using column generation”, European Journal of Operational Research, 164(2): 510-534, (2005).
• [80] Bester, M. J., Nieuwoudt, I., and Van Vuuren, J. H., “Finding good nurse duty schedules: A case study”, Journal of Scheduling, 10 (6): 387-405, (2007).
• [81] Maenhout, B., and Vanhoucke, M., “An electromagnetic meta-heuristic for the nurse scheduling problem”, Journal of Heuristics, 13(4): 359-385, (2007).
• [82] Moz, M., and Vaz Pato, M., “A genetic algorithm approach to a nurse rerostering problem”, Computers & Operations Research, 34(3): 667-691, (2007).
• [83] Purnomo, H. W., and Bard, J. F., “Cyclic preference scheduling for nurses using branch and price”, Naval Research Logistics, 54(2): 200-220, (2007).
• [84] Yeh, J.-Y., and Lin, W.-S., “Using simulation technique and genetic algorithm to improve the quality care of a hospital emergency department”, Expert Systems with Applications, 32(4): 1073-1083, (2007).
• [85] Kundu, S., and Acharyya, S., “A sat approach for solving the nurse scheduling problem”. IEEE Region 10 Annual International Conference, Proceedings/TENCON, (2008).
• [86] Duenas, A., Tütüncü, G. Y., and Chilcott, J. B., “A genetic algorithm approach to the nurse scheduling problem with fuzzy preferences”, IMA Journal of Management Mathematics, 20(4): 369-383, (2009).
• [87] Tsai, C. C., and Li, S. H. A., “A two-stage modeling with genetic algorithms for the nurse scheduling problem”, Expert Systems with Applications, 36(5): 9506-9512, (2009).
• [88] Burke, E. K., Curtois, T., Qu, R., and Vanden Berghe, G., “A scatter search methodology for the nurse rostering problem”, Journal of the Operational Research Society, 61(11): 1667-1679, (2010).
• [89] Burke, E. K., Li, J., and Qu, R., “A pareto-based search methodology for multi-objective nurse scheduling”, Annals of Operations Research, 196(1): 91-109, (2012).
• [90] Shahnazari-Shahrezaei, P., Tavakkoli-Moghaddam, R., Azarkish, M., and Sadeghnejad-Barkousaraie, A., “A differential evolution algorithm developed for a nurse scheduling problem”, South African Journal of Industrial Engineering, 23(3): 68-90, (2012).
• [91] Wu, T. H., Yeh, J. Y., and Lee, Y. M., “A particle swarm optimization approach with refinement procedure for nurse rostering problem”, Computers & Operations Research, 54: 52-63, (2015).
• [92] Altamirano, L., Riff, M. C., Araya, I., and Trilling, L., “Anesthesiology nurse scheduling using particle swarm optimization”, International Journal of Computational Intelligence Systems, 5(1): 111-125, (2012).
• [93] Yin, P. Y., and Chiang, Y. T., “Cyber swarm algorithms for multi-objective nurse rostering problem”, International Journal of Innovative Computing, Information and Control, 9(5): 2043-2063, (2013).
• [94] Lü, Z., and Hao, J. K., “Adaptive neighborhood search for nurse rostering”, European Journal of Operational Research, 218(3): 865-876, (2012).
• [95] Meignan, D., and Knust, S., “A neutrality-based iterated local search for shift scheduling optimization and interactive reoptimization”, European Journal of Operational Research, 279(2): 320-334, (2019).
• [96] Cetin Yagmur, E., and Sarucan, A., “Nurse scheduling with opposition-based parallel harmony search algorithm”, Journal of Intelligent Systems, 28(4): 633-647, (2019).
• [97] Hadwan, M., Ayob, M., Sabar, N. R., and Qu, R., “A harmony search algorithm for nurse rostering problems”, Information Sciences, 233: 126-140, (2013).
• [98] Awadallah, M. A., Khader, A. T., Al-Betar, M. A., and Bolaji, A. L. a., “Harmony search with novel selection methods in memory consideration for nurse rostering problem”, Asia-Pacific Journal of Operational Research, 31(03): 1450014, (2014).
• [99] Todorovic, N., and Petrovic, S., “Bee colony optimization algorithm for nurse rostering”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(2): 467-473, (2012).
• [100] Buyukozkan, K., and Sarucan, A., “Applicability of artificial bee colony algorithm for nurse scheduling problems”, International Journal of Computational Intelligence Systems, 7(sup1): 121-136, (2014).
• [101] Hassani, M. R., and Behnamian, J., “A scenario-based robust optimization with a pessimistic approach for nurse rostering problem”, Journal of Combinatorial Optimization, 41: 143-169, (2021).
• [102] Ceschia, S., Guido, R. and Schaerf, A., “Solving the static inrc-ii nurse rostering problem by simulated annealing based on large neighborhoods”, Annals of Operations Research, 288(1): 95-113, (2020).
• [103] Abuhamdah, A., Boulila, W., Jaradat, G. M., Quteishat, A. M., Alsmadi, M. K. and Almarashdeh, I. A., “A novel population-based local search for nurse rostering problem”, International Journal of Electrical & Computer Engineering (2088-8708), 11(1): (2021).
• [104] Tassopoulos, I. X., Solos, I. P., and Beligiannis, G. N., “Α two-phase adaptive variable neighborhood approach for nurse rostering”, Computers & Operations Research, 60:150-169, (2015).
• [105] Zheng, Z., Liu, X., and Gong, X., “A simple randomized variable neighbourhood search for nurse rostering”, Computers & Industrial Engineering, 110: 165-174, (2017).
• [106] Smet, P., Bilgin, B., De Causmaecker, P., and Vanden Berghe, G., “Modelling and evaluation issues in nurse rostering”, Annals of Operations Research, 218: 303-326, (2014).
• [107] Kheiri, A., Gretsista, A., Keedwell, E., Lulli, G., Epitropakis, M. G., and Burke, E. K., “A hyper-heuristic approach based upon a hidden markov model for the multi-stage nurse rostering problem”, Computers & Operations Research, 130: 105-221, (2021).
• [108] Santos, H. G., Toffolo, T. A., Gomes, R. A., and Ribas, S., “Integer programming techniques for the nurse rostering problem”, Annals of Operations Research, 239: 225-251, (2016).
• [109] Valouxis, C., Gogos, C., Goulas, G., Alefragis, P., and Housos, E., “A systematic two phase approach for the nurse rostering problem”, European Journal of Operational Research, 219(2): 425-433, (2012).
• [110] Haddadi, S., “Three-phase method for nurse rostering”, International Journal of Management Science and Engineering Management, 14(3): 193-205, (2019).
• [111] Guessoum, F., Haddadi, S., and Gattal, E., “Simple, yet fast and effective two-phase method for nurse rostering”, American Journal of Mathematical and Management Sciences, 39(1): 1-19, (2020).
• [112] Turhan, A. M., and Bilgen, B., “A hybrid fix-and-optimize and simulated annealing approaches for nurse rostering problem”, Computers & Industrial Engineering, 145: 106531, (2020).
• [113] Michael, C., Jeffery, C., and David, C., “Nurse preference rostering using agents and iterated local search”, Annals of Operations Research, 226(1): 443-461, (2015).
• [114] Jin, S. H., Yun, H. Y., Jeong, S. J., and Kim, K. S., “Hybrid and cooperative strategies using harmony search and artificial immune systems for solving the nurse rostering problem”, Sustainability, 9(7): 1090, (2017).
• [115] Chen, P. S., and Zeng, Z. Y., “Developing two heuristic algorithms with metaheuristic algorithms to improve solutions of optimization problems with soft and hard constraints: An application to nurse rostering problems”, Applied Soft Computing, 93: 106-336, (2020).
• [116] Cheng, B., Lee, J. H. M., and Wu, J., “A nurse rostering system using constraint programming and redundant modeling”, IEEE Transactions on information technology in biomedicine, 1(1): 44-54, (1997).
• [117] Darmoni, S. J., Fajner, A., Mahe, N., Leforestier, A., Vondracek, M., Stelian, O., and Baldenweck, M., “Horoplan: Computer-assisted nurse scheduling using constraint-based programming”, Journal of the Society for Health Systems, 5(1): 41-54, (1995).
• [118] Okada, M., and Okada, M., “Prolog-based system for nursing staff scheduling implemented on a personal computer”, Computers and Biomedical Research, 21(1): 53-63, (1988).
• [119] Scott, S., and Simpson, R., “Case-bases incorporating scheduling constraint dimensions-experiences in nurse rostering”, European Workshop on Advances in Case-Based Reasoning, 392-401, (1998).
• [120] Osogami, T., and Imai, H., “Classification of various neighborhood operations for the nurse scheduling problem”. International Symposium on Algorithms and Computation, 72-83, (2000).
• [121] Ceschia, S., Di Gaspero, L., Mazzaracchio, V., Policante, G. and Schaerf, A., “Solving a real-world nurse rostering problem by simulated annealing”, Operations Research for Health Care, 36: 100-379, (2023).
• [122] Liu, Z., Liu, Z., Zhu, Z., Shen, Y., and Dong, J., “Simulated annealing for a multi-level nurse rostering problem in hemodialysis service”, Applied Soft Computing, 64: 148-160, (2018).
• [123] Guo, J., and Bard, J. F., “A column generation-based algorithm for midterm nurse scheduling with specialized constraints, preference considerations, and overtime”, Computers & Operations Research, 138: 105-597, (2022).
• [124] Akbarzadeh, B., Moslehi, G., Reisi-Nafchi, M., and Maenhout, B., “The re-planning and scheduling of surgical cases in the operating room department after block release time with resource rescheduling”, European Journal of Operational Research, 278 (2): 596-614, (2019).
• [125] Pandey, A. N., Singh, B. R., Talwar, C. Y., and Srinivasan, D. K., “Figuring and forecasting of hospital beds for patient population arriving at health care institute”: Illustrating roemer’s law. 19th International Conference on Health Informatics and Health Information Management, 7-8, (2017).
• [126] Dantas, T. M., Oliveira, F. L. C., and Repolho, H. M. V., “Air transportation demand forecast through bagging holt winters methods”, Journal of Air Transport Management, 59: 116-123, (2017).
• [127] Tratar, L. F., and Strmčnik, E., “The comparison of holt–winters method and multiple regression method: A case study”, Energy, 109: 266-276, (2016).
• [128] Mitosis, K. D., Lamnisos, D., and Talias, M. A., “Talent management in healthcare: A systematic qualitative review”, Sustainability, 13(8): 4469, (2021).
• [129] Hosseinzadeh Nojedeh, S., and Sattari Ardabili, F., “An overview on talent management in nursing”, Management Issues in Healthcare System, 1: 4-17, (2015).
• [130] Depasse, B., Pauwels, D., Somers, Y., and Vincent, J. L., “A profile of european icu nursing”, Intensive care medicine, 24(9): 939-945, (1998).
• [131] Gill, F. J., Leslie, G. D., Grech, C., and Latour, J. M., “A review of critical care nursing staffing, education and practice standards”, Australian Critical Care, 25(4): 224-237, (2012).
• [132] Ääri, R. L., Tarja, S., and Helena, L. K., “Competence in intensive and critical care nursing: A literature review”, Intensive and critical care nursing, 24(2): 78-89, (2008).
• [133] Yeh, S. H., Hsiao, C. Y., Ho, T. H., Chiang, M. C., Lin, L. W., Hsu, C. Y. and Lin, S. Y., “The effects of continuing education in restraint reduction on novice nurses in intensive care units”, The journal of nursing research: JNR, 12(3): 246-256, (2004).
• [134] Ergün, Y. A., Demir, H., and Sağnak, N., “Yoğun bakim hemşirelerinin görev tanimlari ile ilgili çalişma”, Yoğun bakım hemşireliği dergisi, 11(2): 102-113, (2007).
• [135] Alexander, M. F., and Runciman, P. J., Icn framework of competencies for the generalist nurse: Report of the development process and consultation International Council of Nurses, (2003).
• [136] Donovan, A. L., Aldrich, J. M., Gross, A. K., Barchas, D. M., Thornton, K. C., Schell-Chaple, H. M., Gropper, M. A., and Lipshutz,A. K., “Interprofessional care and teamwork in the icu”, Critical care medicine, 46(6): 980-990, (2018).
• [137] Hicks, F. D., Merritt, S. L., and Elstein, A. S., “Critical thinking and clinical decision making in critical care nursing: A pilot study”, Heart & Lung, 32(3): 169-180, (2003).
• [138] Reader, T. W., Flin, R., and Cuthbertson, B. H., “Communication skills and error in the intensive care unit”, Current opinion in critical care, 13(6): 732-736, (2007).
• [139] Pronovost, P., Wu, A. W., Dorman, T., and Morlock, L., “Building safety into icu care”, Journal of critical care, 17(2): 78-85, (2002).
• [140] Wang, J. J., and Yang, D. L., “Using a hybrid multi-criteria decision aid method for information systems outsourcing”, Computers & Operations Research, 34(12): 3691-3700, (2007).
• [141] Hshiung Tzeng, G., and Jeng Huang, J., “Multiple attribute decision making methods and applications, (2011).
• [142] Krishankumar, R., Ramprakash, R., Premaladha, J., and Ravichandran, K. S., “Solving head nurse selection problem using hybrid vikor method under triangular fuzzy environment”. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), 647-652, (2017).
• [143] Tavana, M., Kennedy, D. T., and Joglekar, P., “A group decision support framework for consensus ranking of technical manager candidates”, Omega, 24(5): 523-538, (1996).
• [144] Thakkar, J. J., “Analytic hierarchy process (ahp)”. In Studies in Systems, Decision and Control. pp. 33-62, (2021).
• [145] Hwang, C. L., and Masud, A. S. M., “Multiple objective decision making—methods and applications”: A state-of-the-art survey Springer Science & Business Media, (2012).
• [146] Dağdeviren, M., Yavuz, S., and Kılınç, N., “Weapon selection using the ahp and topsis methods under fuzzy environment”, Expert systems with applications, 36(4): 8143-8151, (2009).
• [147] Yoon, K., and Hwang, C., “Topsis (technique for order preference by similarity to ideal solution)–a multiple attribute decision making, w: Multiple attribute decision making–methods and applications, a state-of-the-at survey”, Berlin: Springer Verlag, (1981).
• [148] Wu, M., “Topsis-ahp simulation model and its application to supply chain management”, World Journal of Modelling and Simulation, 3(3): 196-201, (2007).
• [149] Li, Y., Liu, X., and Chen, Y., “Supplier selection using axiomatic fuzzy set and topsis methodology in supply chain management”, Fuzzy Optimization and Decision Making, 11(2): 147-176, (2012).
• [150] Ortiz‐Barrios, M., Miranda‐De la Hoz, C., López‐Meza, P., Petrillo, A. and De Felice, F., “A case of food supply chain management with ahp, dematel, and topsis”, Journal of Multi‐Criteria Decision Analysis, 27(1-2): 104-128, (2020).
• [151] Ertuğrul, İ. and Karakaşoğlu, N., “Performance evaluation of turkish cement firms with fuzzy analytic hierarchy process and topsis methods”, Expert Systems with Applications, 36(1): 702-715, (2009).
• [152] Zulqarnain, R. M., Xin, X. L., Saeed, M., Ahmad, N., Dayan, F., and Ahmad, B., “Recruitment of medical staff in health department by using topsis method”, International Journal of Pharmaceutical Sciences Review and Research, 62(1): 1-7, (2020).
• [153] Araujo, C. A. S., Wanke, P., and Siqueira, M. M., “A performance analysis of brazilian public health: Topsis and neural networks application”, International Journal of Productivity and Performance Management, (2018).
• [154] Abolhallaje, M., Mousavi, S. M., Anjomshoa, M., Nasiri, A. B., Seyedin, H., Sadeghifar, J., Aryankhesal, A., Vasokolaei, G. R., and Nasiri, M. B., “Assessing health inequalities in iran: A focus on the distribution of health care facilities”, Global journal of health science, 6(4): 285, (2014).
• [155] Rao, R. V., “Decision making in the manufacturing environment: Using graph theory and fuzzy multiple attribute decision making methods”, Springer Science & Business Media, (2007).
• [156] Kwok, P. K., and Lau, H. Y., “Hotel selection using a modified topsis-based decision support algorithm”, Decision Support Systems, 120: 95-105, (2019).
• [157] Rosenthal, R. E., “Gams--a user's guide”, (2004).
• [158] Chatfield, C., and Yar, M., “Holt‐winters forecasting: Some practical issues”, Journal of the Royal Statistical Society: Series D (The Statistician), 37(2): 129-140, (1988).
• [159] Rasmussen, R., “On time series data and optimal parameters”, Omega, 32(2): 111-120, (2004).