سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,501
تعداد مشاهده مقاله124,115,191
تعداد دریافت فایل اصل مقاله97,219,202
Mahdizadeh Sarami, Reza, Bashirzadeh, Reza, Ramezanian, Reza. (1403). A fuzzy Multi-Objective Model for Surgical Staff Considering Frequency and Fairness in Time Allocation: A Case Study. , 58(1), 125-158. doi: 10.22059/aie.2024.369867.1882
Reza Mahdizadeh Sarami; Reza Bashirzadeh; Reza Ramezanian. "A fuzzy Multi-Objective Model for Surgical Staff Considering Frequency and Fairness in Time Allocation: A Case Study". , 58, 1, 1403, 125-158. doi: 10.22059/aie.2024.369867.1882
Mahdizadeh Sarami, Reza, Bashirzadeh, Reza, Ramezanian, Reza. (1403). 'A fuzzy Multi-Objective Model for Surgical Staff Considering Frequency and Fairness in Time Allocation: A Case Study', , 58(1), pp. 125-158. doi: 10.22059/aie.2024.369867.1882
Mahdizadeh Sarami, Reza, Bashirzadeh, Reza, Ramezanian, Reza. A fuzzy Multi-Objective Model for Surgical Staff Considering Frequency and Fairness in Time Allocation: A Case Study. , 1403; 58(1): 125-158. doi: 10.22059/aie.2024.369867.1882

A fuzzy Multi-Objective Model for Surgical Staff Considering Frequency and Fairness in Time Allocation: A Case Study

Advances in Industrial Engineering
دوره 58، شماره 1، شهریور 2024، صفحه 125-158    اصل مقاله (1.75 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/aie.2024.369867.1882
نویسندگان
Reza Mahdizadeh Sarami1؛ Reza Bashirzadeh2؛ Reza Ramezanian* 3
1M.Sc., Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
2Assistant Professor, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
3Associate Professor, Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.
چکیده
Nurses’ scheduling problems have attracted a significant amount of healthcare research, indicating the importance of these issues. In this paper, it has tried to present a multi-objective model for the assignment of nurses and anesthesiologists to surgical teams, considering frequency and fairness in allocating time to staff members. Since idle time is inevitable, we seek to divide idle time equally among staff. In addition, the break time of each staff member have an almost regular frequency during the shift. Minimizing overtime costs and maximizing attention to the willingness of surgical staff members to work overtime are other objectives of the problem. Three metaheuristic algorithms NSGA-II, MOPSO, and SPEA-II used to solve the presented model. A hybrid multi-objective genetic algorithm based on variable neighborhood search is also presented. The comparison of the solutions of 4 algorithms shows that the proposed hybrid algorithm has a significant superiority compared to other algorithms in terms of the average value of the solution, the quality of the Pareto solution set, and execution time. The presented model is compared with the real data of the surgical department of elective patients of a government hospital in Qazvin province. The obtained results show that the presented model has significantly created equality in the amount of working time of nurses and anesthesiologists in the elective surgery department. It has also spread the idle time of each staff member during the work shift, which has caused different time breaks for each one.
کلیدواژه‌ها
Break Time Frequency؛ Fuzzy Time Duration؛ Heuristic Algorithm؛ Idle Time Fairness؛ Nurse Scheduling
مراجع
 

  1. Abdelghany, M., Eltawil, A. B., Yahia, Z., & Nakata, K. (2021). A hybrid variable neighbourhood search and dynamic programming approach for the nurse rostering problem. Journal of Industrial and Management Optimization, 17(4), 2051-2072.
  2. Adriaenssens, J., De Gucht, V., & Maes, S. (2015). Determinants and prevalence of burnout in emergency nurses: a systematic review of 25 years of research. International journal of nursing studies, 52(2), 649-661.
  3. Adynski, G. I., Sherwood, G., Ikharo, E., Tran, A., & Jones, C. B. (2022). Outpatient nurse staffing relationship with organizational, nurse and patient outcomes: A scoping review. International Journal of Nursing Studies Advances, 4, 100064.
  4. Afsar, S., Palacios, J. J., Puente, J., Vela, C. R., & Gonzalez-Rodriguez, I. (2022). Multi-objective enhanced memetic algorithm for green job shop scheduling with uncertain times. Swarm and Evolutionary Computation, 68, 101016.
  5. Aiken, L. H., Clarke, S. P., Sloane, D. M., Sochalski, J., & Silber, J. H. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. Jama, 288(16), 1987-1993.
  6. Ajami, S., Ketabi, S., Yarmohammadian, M. H., & Bagherian, H. (2012). Wait time in emergency department (ED) processes. Medical Archives, 66(1), 53.
  7. Al‐Hussami, M., Darawad, M., Saleh, A., & Hayajneh, F. A. (2014). Predicting nurses' turnover intentions by demographic characteristics, perception of health, quality of work attitudes. International journal of nursing practice, 20(1), 79-88.
  8. Amindoust, A., Asadpour, M., & Shirmohammadi, S. (2021). A hybrid genetic algorithm for nurse scheduling problem considering the fatigue factor. Journal of Healthcare Engineering, 2021.
  9. Azaiez, M. N., & Al Sharif, S. S. (2005). A 0-1 goal programming model for nurse scheduling. Computers & Operations Research, 32(3), 491-507.
  10. 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.
  11. Bard, J. F., & Purnomo, H. W. (2005). Short-term nurse scheduling in response to daily fluctuations in supply and demand. Health Care Management Science, 8, 315-324.
  12. Bialous, S. A., & Baltzell, K. (2020). The world needs 6 million more nurses: what are we waiting for?. The American journal of tropical medicine and hygiene, 103(1), 1.
  13. Buchan, J., & Aiken, L. (2008). Solving nursing shortages: a common priority. Journal of clinical nursing, 17(24), 3262-3268.
  14. Buchan, J., Duffield, C., & Jordan, A. (2015). ‘Solving’nursing shortages: do we need a New Agenda?. Journal of Nursing Management, 23(5), 543-545.
  15. Ceschia, S., Dang, N., De Causmaecker, P., Haspeslagh, S., & Schaerf, A. (2019). The second international nurse rostering competition. Annals of Operations Research, 274(1-2), 171-186.
  16. Chan, Z. C., Tam, W. S., Lung, M. K., Wong, W. Y., & Chau, C. W. (2013). A systematic literature review of nurse shortage and the intention to leave. Journal of nursing management, 21(4), 605-613.
  17. Chiaramonte, M. V., & Chiaramonte, L. M. (2008). An agent-based nurse rostering system under minimal staffing conditions. International Journal of Production Economics, 114(2), 697-713.
  18. Coomber, B., & Barriball, K. L. (2007). Impact of job satisfaction components on intent to leave and turnover for hospital-based nurses: a review of the research literature. International journal of nursing studies, 44(2), 297-314.
  19. Darawad, M. W., Nawafleh, H., Maharmeh, M., Hamdan-Mansour, A. M., & Azzeghaiby, S. N. (2015). The relationship between time pressure and burnout syndrome: A cross-sectional survey among Jordanian nurses. Health, 7(01), 14.
  20. Das, A. K., & Pratihar, D. K. (2019). A novel approach for neuro-fuzzy system-based multi-objective optimization to capture inherent fuzziness in engineering processes. Knowledge-Based Systems, 175, 1-11.
  21. Deb, K. (2001). Nonlinear goal programming using multi-objective genetic algorithms. Journal of the Operational Research Society, 52, 291-302.
  22. Di Martinelly, C., & Meskens, N. (2014, March). Building Surgical Team with High Affinities. In Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems: SCITEPRESS-Science and Technology Publications. LDA (pp. 417-424).
  23. Di Martinelly, C., & Meskens, N. (2017). A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses' idle time. International Journal of Production Economics, 191, 323-334.
  24. Díaz-Alonso, J., Fernández-Feito, A., Forjaz, M. J., Andina-Díaz, E., García-Cueto, E., & Lana, A. (2022). Contributions of nursing students during their clinical practice in primary care: Adaptation and validation of a scale. Nurse education in practice, 65, 103496.
  25. Duka, E. (2014). Nurse scheduling problem (No. 1410). Technical University of Cluj Napoca, Department of Economics and Physics.
  26. 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.
  27. Escaffi Schwarz, M. (2021). Developing and Testing an Episodic Model of Work Breaks (Doctoral dissertation, Manchester Business School).
  28. Fan, J., & Smith, A. P. (2019). Mental workload and other causes of different types of fatigue in rail staff. In Human Mental Workload: Models and Applications: Second International Symposium, H-WORKLOAD 2018, Amsterdam, The Netherlands, September 20-21, 2018, Revised Selected Papers 2 (pp. 147-159). Springer International Publishing.
  29. Fishbein, D., Nambiar, S., McKenzie, K., Mayorga, M., Miller, K., Tran, K., ... & Capan, M. (2019). Objective measures of workload in healthcare: a narrative review. International Journal of Health Care Quality Assurance, 33(1), 1-17.
  30. Fritz, C., Lam, C. F., & Spreitzer, G. M. (2011). It's the little things that matter: An examination of knowledge workers' energy management. Academy of Management Perspectives, 25(3), 28-39.
  31. Gifkins, J., Johnston, A., Loudoun, R., & Troth, A. (2020). Fatigue and recovery in shiftworking nurses: A scoping literature review. International journal of nursing Studies, 112, 103710.
  32. Gomathi, R., & Sharmila, D. (2014). A novel adaptive cuckoo search for optimal query plan generation. The Scientific World Journal, 2014.
  33. Guo, J., & Bard, J. F. (2022). A column generation-based algorithm for midterm nurse scheduling with specialized constraints, preference considerations, and overtime. Computers & Operations Research, 138, 105597.
  34. Gupta, C. C., Coates, A. M., Dorrian, J., & Banks, S. (2019). The factors influencing the eating behaviour of shiftworkers: What, when, where and why. Industrial health, 57(4), 419-453.
  35. 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.
  36. Hamid, M., Tavakkoli-Moghaddam, R., Vahedi-Nouri, B., & Arbabi, H. (2020). A mathematical model for integrated operating room and surgical member scheduling considering lunch break. International journal of research in industrial engineering, 9(4), 304-312.
  37. Hunsaker, S., Chen, H. C., Maughan, D., & Heaston, S. (2015). Factors that influence the development of compassion fatigue, burnout, and compassion satisfaction in emergency department nurses. Journal of nursing scholarship, 47(2), 186-194.
  38. Jaumard, B., Semet, F., & Vovor, T. (1998). A generalized linear programming model for nurse scheduling. European journal of operational research, 107(1), 1-18.
  39. Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European journal of operational research, 177(3), 1599-1609.
  40. Kaur, M. N., & Gujral, H. K. Associated Aid of Rest and Restorative Sleep in Nurses: A Conceptual Approach.
  41. Khairunnisa, Z. A., Bahri, S., & Effendy, S. (2021). Effect of Workload, Compensation, and Motivation on Employee Performance at Madani Medan General Hospital. Budapest International Research and Critics Institute-Journal (BIRCI-Journal), 4(4), 10807-10813.
  42. 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.
  43. 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.
  44. Kong, M., Xu, J., Zhang, T., Lu, S., Fang, C., & Mladenovic, N. (2021). Energy-efficient rescheduling with time-of-use energy cost: Application of variable neighborhood search algorithm. Computers & Industrial Engineering, 156, 107286.
  45. Li, L., Zhang, Q., & Yang, H. (2022). Incidence and related influencing factors of workplace violence among psychiatric nurses in China: a systematic review and meta-analysis. Archives of psychiatric nursing, 40, 68-76.
  46. Lim, G. J., Mobasher, A., & Côté, M. J. (2012). Multi-objective nurse scheduling models with patient workload and nurse preferences. Management, 2(5), 149-160.
  47. Lim, G. J., Mobasher, A., Bard, J. F., & Najjarbashi, A. (2016). Nurse scheduling with lunch break assignments in operating suites. Operations Research for Health Care, 10, 35-48.
  48. Liu, S., Wang, C., Jiang, Y., Ren, H., Yu, T., Cun, W., & Yang, Z. (2022). Nurse scheduling in COVID‐19‐designated hospitals in China: A nationwide cross‐sectional survey. Journal of Nursing Management, 30(8), 4024-4033.
  49. Luger, T., Bonsch, R., Seibt, R., Krämer, B., Rieger, M. A., & Steinhilber, B. (2023). Intraoperative active and passive breaks during minimally invasive surgery influence upper extremity physical strain and physical stress response—A controlled, randomized cross-over, laboratory trial. Surgical Endoscopy, 1-14.
  50. Lyubykh, Z., Gulseren, D., Premji, Z., Wingate, T. G., Deng, C., Bélanger, L. J., & Turner, N. (2022). Role of work breaks in well-being and performance: A systematic review and future research agenda. Journal of Occupational Health Psychology, 27(5), 470.
  51. Martin, D. M. (2015). Nurse fatigue and shift length: a pilot study. Nursing economics, 33(2), 81.
  52. McDermid, F., Mannix, J., & Peters, K. (2020). Factors contributing to high turnover rates of emergency nurses: A review of the literature. Australian Critical Care, 33(4), 390-396.
  53. Meskens, N., Duvivier, D., & Hanset, A. (2013). Multi-objective operating room scheduling considering desiderata of the surgical team. Decision Support Systems, 55(2), 650-659.
  54. Min, A., Min, H., & Hong, H. C. (2019). Work schedule characteristics and fatigue among rotating shift nurses in hospital setting: An integrative review. Journal of nursing management, 27(5), 884-895.
  55. Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & operations research, 24(11), 1097-1100.
  56. Montazeri, M., Multmeier, J., Novorol, C., Upadhyay, S., Wicks, P., & Gilbert, S. (2021). Optimization of patient flow in urgent care centers using a digital tool for recording patient symptoms and history: simulation study. JMIR Formative Research, 5(5), e26402.
  57. Mrayyan, M. T., & Hamaideh, S. H. (2009). Clinical errors, nursing shortage and moral distress: The situation in Jordan. Journal of Research in Nursing, 14(4), 319-330.
  58. Mustaffa, K. H., Shafie, A. A., & Ngu, L. H. (2022). A Comparison of Self-evaluated Survey and Work Sampling Approach for Estimating Patient-care Unit Cost Multiplier in Genetic Nursing Activities. Asian Nursing Research, 16(3), 170-179.
  59. 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.
  60. Nurmine, K. (2022). Work stress among registered nurses in health care services: descriptive literature review.
  61. Ondrejková, N., & Halamová, J. (2022). Qualitative analysis of compassion fatigue and coping strategies among nurses. International journal of nursing sciences, 9(4), 467-480.
  62. Pahlevanzadeh, M. J., Jolai, F., Goodarzian, F., & Ghasemi, P. (2021). A new two-stage nurse scheduling approach based on occupational justice considering assurance attendance in works shifts by using Z-number method: A real case study. RAIRO-Operations Research, 55(6), 3317-3338.
  63. Riklikienė, O., Didenko, O., Čiutienė, R., Daunorienė, A., & Čiarnienė, R. (2020). Balancing nurses’ workload: A case study with nurse anaesthetists and intensive care nurses. Economics and Sociology, 11-25.
  64. Sagherian, K., Cho, H., & Steege, L. M. (2022). The insomnia, fatigue, and psychological well‐being of hospital nurses 18 months after the COVID‐19 pandemic began: A cross‐sectional study. Journal of clinical nursing.
  65. Salvage, J., & White, J. (2020). Our future is global: nursing leadership and global health. Revista latino-americana de enfermagem, 28.
  66. Scheffler, R. M., & Arnold, D. R. (2019). Projecting shortages and surpluses of doctors and nurses in the OECD: what looms ahead. Health Economics, Policy and Law, 14(2), 274-290.
  67. Senbel, S. (2021). A Fairness-Based Heuristic Technique for Long-Term Nurse Scheduling. Asia-Pacific Journal of Operational Research, 38(02), 2050047.
  68. Sharifi, S. S., Attari, M. Y. N., Torkayesh, A. E., & Jami, E. N. (2021). Nursing service planning for the satisfaction of patients: models and algorithms. International Journal of Services and Operations Management, 39(3), 377-398.
  69. Srinivas, S., & Ravindran, A. R. (2020). Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers. Health care management science, 23, 360-386.
  70. 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.
  71. Swiger, P. A., Vance, D. E., & Patrician, P. A. (2016). Nursing workload in the acute-care setting: A concept analysis of nursing workload. Nursing outlook, 64(3), 244-254.
  72. Tan, S., & Sun, H. (2016). Comparing Two Shift Patterns of Nurse Scheduling in Chinese ICUs. In Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015: Core Theory and Applications of Industrial Engineering (Volume 1) (pp. 219-228). Atlantis Press.
  73. 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.
  74. Topaloglu, S., & Selim, H. (2010). Nurse scheduling using fuzzy modeling approach. Fuzzy sets and systems, 161(11), 1543-1563.
  75. Tsai, C. C., & Li, S. H. (2009). A two-stage modeling with genetic algorithms for the nurse scheduling problem. Expert systems with applications, 36(5), 9506-9512.
  76. Valouxis, C., & Housos, E. (2000). Hybrid optimization techniques for the workshift and rest assignment of nursing personnel. Artificial Intelligence in Medicine, 20(2), 155-175.
  77. Valouxis, C., Gogos, C., Goulas, G., Alefragis, P., & Housos, E. (2012). A systematic two phase approach for the nurse rostering problem. European Journal of Operational Research, 219(2), 425-433.
  78. Van Dyck, S. E. (2021). An Examination of Nurses’ Schedule Characteristics, Recovery from Work, and Well-Being (Doctoral dissertation, Portland State University).
  79. Walton, K. (2021). Pediatric Nurses' Perception of Workload in a Large System Infusion Center.
  80. Wang, J. J., Dai, Z., Chang, A. C., & Shi, J. J. (2022). Surgical scheduling by Fuzzy model considering inpatient beds shortage under uncertain surgery durations. Annals of Operations Research, 315(1), 463-505.
  81. Wang, K., Qin, H., Huang, Y., Luo, M., & Zhou, L. (2021). Surgery scheduling in outpatient procedure centre with re-entrant patient flow and fuzzy service times. Omega, 102, 102350.
  82. Wang, W. (2017). A Two-Stage Dynamic Programming Model for Nurse Rostering Problem Under Uncertainty (Doctoral dissertation).
  83. Wang, Z., You, L., Zheng, J., & Guan, X. (2017). The ABCD patient classification tool for nurse-to-patient assignment to improve nursing workload balance: a multi-center study. International Journal of Clinical And Experimental Medicine, 10(6), 9502-9507.
  84. Warner, D. M. (1976). Scheduling nursing personnel according to nursing preference: A mathematical programming approach. Operations Research, 24(5), 842-856.
  85. Welp, A., Meier, L. L., & Manser, T. (2015). Emotional exhaustion and workload predict clinician-rated and objective patient safety. Frontiers in psychology, 5, 1573.
  86. Wolbeck, L. A. (2019). Fairness aspects in personnel scheduling.
  87. Wu, D., Cui, W., Wang, X., Huo, Y., Yu, G., & Chen, J. (2021). Improvement in outpatient services using the WeChat calling system in the Shanghai Children’s Hospital. Pakistan Journal of Medical Sciences, 37(4), 993.
  88. Yilmaz, E. (2012). A mathematical programming model for scheduling of nurses’ labor shifts. Journal of medical systems, 36(2), 491-496.
  89. Yousefi Nejad Attari, M., Ebadi Torkayesh, S., & Ebadi Torkayesh, A. (2020). Staff Assignment in Emergency Department Considering Service Time. Journal of Quality Engineering and Production Optimization, 5(2), 105-128.
  90. Zhang, Z., Hao, Z., & Huang, H. (2011). Hybrid swarm-based optimization algorithm of GA & VNS for nurse scheduling problem. In Information Computing and Applications: Second International Conference, ICICA 2011, Qinhuangdao, China, October 28-31, 2011. Proceedings 2 (pp. 375-382). Springer Berlin Heidelberg.
  91. Zhu, Y. H., Vancroonenburg, W., & Vanden Berghe, G. (2019). Managing nurse workload by planning patient-to-ward assignments. In ORBEL 33, Date: 2019/02/07-2019/02/08, Location: Hasselt University (UHasselt).
آمار
تعداد مشاهده مقاله: 375
تعداد دریافت فایل اصل مقاله: 90


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب