زمان‌بندی اتاق‌های عمل با در نظر گرفتن تسهیلات سیار و تخصص پزشکان

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران.

2 دانشیار، گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران.

3 دانشجوی دکتری، گروه مهندسی صنایع، دانشکده مهندسی صنایع، دانشگاه علم و صنعت، تهران، ایران.

4 استادیار، گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه صنعتی قم، قم، ایران.

چکیده

هدف: یکی از عناصر مهم بخش سلامت، بیمارستان‌ها هستند و اتاق‌های عمل از مهم‌ترین و پرهزینه‌ترین بخش‌های آن به‌شمار می‌آیند. امروزه تسهیلات سیار، به کاهش بار کاری بیمارستان‌ها کمک شایانی می‌کند، بنابراین استقرار تسهیلات سیار در مکانی مناسب، همراه با تسهیلات ثابت و زمان‌بندی مناسب آنها قادر است تا حد زیادی مشکلات سلامت جامعه را حل کند. در این مقاله، یک مدل یکپارچه مکان‌یابی ـ تخصیص و زمان‌بندی اتاق عمل، برای استقرار تسهیلات سیار در مکان مناسب ارائه شده است تا با بهره‌بردن از آن، گامی در جهت کاهش بار کاری تسهیلات ثابت، تخصیص بیماران به تسهیلات و اتاق‌های عمل و زمان‌بندی اتاق‌های عمل با اهداف کاهش هزینه‌ها و کاهش زمان اضافه‌کاری اتاق‌های عمل و پزشکان برداشته شود. شایان ذکر است که در پژوهش حاضر، این موضوع در نظر گرفته شده است که پزشکان دارای حداکثر زمان کاری در هر روز هستند و بعد این ساعات، کارایی عملکرد آنان کاهش می‌یابد.
روش: با توجه به NP-hard بودن مدل و ناتوانی روش‌های دقیق برای حل مسائل در مقیاس بزرگ، یک روش فراابتکاری مبتنی بر الگوریتم ژنتیک برای مسئله توسعه داده شده و کارایی آن در مقیاس‌های گسترده و چندین نمونه بررسی شده است. مسئله در دو حالت تک‌هدفه و دوهدفه تحلیل و نتایج آن گزارش شده است.
یافته‌ها: ﻧﺘﺎﻳﺞ ﻣﺤﺎﺳﺒﺎﺗﻲ حالت تک‌هدفه، روی مثال‌های ﻋﺪدی توسعه داده‌شده در چند مقاله ﻧﺸﺎن داد که اﻟﮕﻮرﻳﺘﻢ اﺑﺘﻜﺎری این پژوهش، در ﺗﻮﻟﻴﺪ پاسخ‌های با دقت بالا کارایی مناسبی دارد. همچنین، در حالت دوهدفه نقاط پارتو مناسبی توسط الگوریتم یافت شده است.
نتیجه‌گیری: نتایج مسئله، گویای اهمیت مکان‌یابی و استقرار تسهیلات سیار و زمان‌بندی یکپارچه آنها، کاهش مدت زمان انتظار بیماران و فاصله آنها با تسهیلات است. الگوریتم پیشنهادی قادر است مسائلی با ابعاد بزرگ را در زمانی کم و جوابی کارا حل کند.

کلیدواژه‌ها


عنوان مقاله [English]

Operating Room Scheduling with Respect to Dynamic Facilities and Surgeon Specialty

نویسندگان [English]

  • Seyyed Jamaloddin Hosseini 1
  • Jalal Rezaeenoor 2
  • Amir Hosein Akbari 3
  • Mohammad Reza Marjani 4
1 Associate Prof., Department of Industrial Engineering, Faculty of Industrial Engineering, University of Qom, Qom, Iran.
2 Associate Prof., Department of Industrial Engineering, Faculty of Industrial Engineering, University of Qom, Qom, Iran.
3 Ph.D Candidate, Department of Industrial Engineering, University of Science and Technology, Tehran, Iran.
4 Assistant Prof., Department of Industrial Engineering, Faculty of Industrial Engineering, University of Qom, Qom, Iran.
چکیده [English]

Objective: Hospitals are one of the most important elements of the health sector, which is the most important and costly part of them are operating rooms. Therefore, locating and allocating resources appropriately and appropriate scheduling for the operating room, can greatly solve the health problems of the community. In this article, an integrated model for locating-allocating and scheduling the operating rooms to build facilities at the appropriate places, allocating patients to facilities and operating rooms and scheduling operating rooms with the goals of reducing costs and reducing the overtime of the operating rooms, is presented. Also, surgeons have the maximum working time per day, and then their performance is faced with an error.
Methods: Given the NP-hard of the model and the inability of accurate methods for solving large-scale problems, a metaheuristic method based on the genetic algorithm has been developed for the problem and its efficiency has been studied in a wide range of samples examples.
Results: Computational results showed that the proposed algorithms can effectively help to generate reasonable responses.
Conclusion: The results of the problem indicate the importance of locating and integrating scheduling of facilities in reducing the waiting time of patients and their distance from facilities. The proposed algorithm is also capable of solving large-scale problems at an efficient response.

کلیدواژه‌ها [English]

  • operating room scheduling
  • operating room locating
  • Genetic Algorithm
  • locating-allocating
جقتایی نوایی، مهدی؛ رجب‌زاده، محسن؛ بزرگی امیری، علی (1395). مکان‎یابی و تخصیص خدمات بیمارستانی با در نظر گرفتن معیارهای هزینه و کارایی: مطالعه موردی شهرستان آمل. فصلنامه مدیریت سلامت، ۱۹ (۶۳)، 21-33.
دباغ، علی؛ اکبری، محمداسماعیل؛ فتحی، محمد (1385). بررسی الگوهای خطاهای پزشکی در سیستم‌های سلامت. فصلنامه مجله دانشگاه علوم پزشکی ارتش جمهوری اسلامی ایران، 3(15)، 957- 966.
کاووسی، زهرا؛ ستوده زاده، فاطمه؛ فردید، مژگان؛ غلامی، مریم؛ خجسته فر، مرضیه؛ حاتم، محبوبه؛ زهرا تحیتی، زهرا؛ غلامرضا، فرهادی (1396). بررسی خطاهای فرایندهای اتاق عمل بیمارستان نمازی با روش تحلیل حالات و اثرات خطا. فصلنامه بیمارستان، ۱۶ (۳)، 57- 70.
References
Aeinparast, A. (1998). Methods of determining costs of patients based on diagnosis related group (DRG). Journal of Health Administration, 2(3), 94-105.
Afshari, H., & Peng, Q. (2014). Challenges and solutions for location of healthcare facilities. Industrial Engineering & Management, 3(1), 12.
Altay, N., Green, W.G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175 (1), pp. 475-493
Aringhieri, R., Bruni, M.E., Khodaparasti, S. (2017). Emergency medical services and beyond: addressing new challenges through a wide literature review. Computers & Operations Research, 78, 349-368.
Aringhieri, R., Landa, P., Soriano, P., Tànfani, E., & Testi, A. (2015). A two level metaheuristic for the operating room scheduling and assignment problem. Computers & Operations Research, 54, 21-34.
Bai, M. (2017). Optimization of Surgery Scheduling in Multiple Operating Rooms with Post Anesthesia Care Unit Capacity Constraints.
Başar, A., Çatay, B., Ünlüyurt, T. (2012). A taxonomy for emergency service station location problem. Optimization Letters, 6, 1147-1160
Bélanger, V., Ruiz, A., Soriano, P. (2019). Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles. European Journal of Operational Research, 272 (1), 1-23.
Belkhamsa, M., Jarboui, B., & Masmoudi, M. (2018). Two metaheuristics for solving no-wait operating room surgery scheduling problem under various resource constraints. Computers & Industrial Engineering, 126, 494-506.
Bernstein, J., MacCourt, D., & Abramson, B. D. (2008). Topics in medical economics: Medical malpractice. The Journal of Bone & Joint Surgery, 90(8), 1777-1782.
Cardoen, B., Demeulemeester, E., & Beliën, J. (2010). Operating room planning and scheduling: A literature review. European journal of operational research, 201(3), 921-932.
Cramer, H., Pohlabeln, H., & Habermann, M. (2013). Factors causing or influencing nursing errors as perceived by nurses: findings of a cross-sectional study in German nursing homes and hospitals. Journal of Public Health, 21(2), 145-153.
Dabagh, A., Akbari, A. M., & Fathi, M. (2006). Medical errors in the health system. Annals of Military and Health Scences Research, 3(15), 957- 966. (in Persian)
Daskin, M. S., & Dean, L. K. (2005). Location of health care facilities. In Operations research and health care (pp. 43-76). Springer, Boston, MA.
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.
Galindo, G., Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research, 230 (2), 201-211
Green, L.V., Kolesar, P. (2004). Improving emergency responsiveness with management science. Management Science, 50 (8), 1001-1014.
Guerriero, F., & Guido, R. (2011). Operational research in the management of the operating theatre: a survey. Health care management science, 14(1), 89-114.
Heidari-Fathian, H., Pasandideh, S.H.R. (2018). Green-Blood supply chain network design: Robust optimization, Bounded Objective Function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95-105.
Hodgson, M. J. (1988). An hierarchical location-allocation model for primary health care delivery in a developing area. Social Science & Medicine, 26(1), 153-161.
Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
Jaghtaei Navaee, J. M., Rajabzadeh, M., & Bozorgi Amiri, A. (2016). locating and allocating hospital service on the basis of cost and efficiency: case study of amol. (in Persian)
Jebali, A., Alouane, A. B. H., & Ladet, P. (2006). Operating rooms scheduling. International Journal of Production Economics, 99(1-2), 52-62.
Kavosi, Z., Setoodehzadeh, F., Fardid, M., Gholami, M., Khojastefar, M., Hatam, M., & Fardid, G. (2017). Risk Assessment of the Processes of Operating Room Department using the Failure Mode and Effects Analysis (FMEA) Method. Journal of Hospital, 16(3), 57-70. (in Persian)
Landa, P., Aringhieri, R., Soriano, P., Tànfani, E., & Testi, A. (2016). A hybrid optimization algorithm for surgeries scheduling. Operations Research for Health Care, 8, 103-114.
Liu, Y., Chu, C., & Wang, K. (2011). A new heuristic algorithm for the operating room scheduling problem. Computers & Industrial Engineering, 61(3), 865-871.
Marques, I., Captivo, M. E., & Pato, M. V. (2012). An integer programming approach to elective surgery scheduling. OR spectrum, 34(2), 407-427.
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.
Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. (2012). Organizing hospitals into networks: a hierarchical and multiservice model to define location, supply and referrals in planned hospital systems. OR spectrum, 34(2), 319-348.
Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. P. (2015). Location–allocation approaches for hospital network planning under uncertainty. European Journal of Operational Research, 240(3), 791-806.
Minas, J.P., Simpson, N.C., Tacheva, Z.Y. (2020). Modeling emergency response operations: a theory building survey. Computers & Operations Research, 119, 104921.
Mitropoulos, P., Mitropoulos, I., & Giannikos, I. (2013). Combining DEA with location analysis for the effective consolidation of services in the health sector. Computers & Operations Research, 40(9), 2241-2250.
Molina-Pariente, J. M., Fernandez-Viagas, V., & Framinan, J. M. (2015). Integrated operating room planning and scheduling problem with assistant surgeon dependent surgery durations. Computers & Industrial Engineering, 82, 8-20.
Nagpal, K., Vats, A., Ahmed, K., Smith, A. B., Sevdalis, N., Jonannsson, H., ... & Moorthy, K. (2010). A systematic quantitative assessment of risks associated with poor communication in surgical care. Archives of surgery, 145(6), 582-588.
Phillips, N. (2016). Berry & Kohn's operating room technique. (14th ed). Elsevier Health Sciences.
Rahman, S. U., & Smith, D. K. (1999). Deployment of rural health facilities in a developing country. Journal of the Operational Research Society, 50(9), 892-902.
Reuter-Oppermann, M., van den Berg, P.L., Vile, J.L. (2017). Logistics for emergency medical service systems. Health Systems, 6 (3), 187-208.
 Rogers, A. E., Hwang, W. T., Scott, L. D., Aiken, L. H., & Dinges, D. F. (2004). The working hours of hospital staff nurses and patient safety. Health affairs, 23(4), 202-212.
Roland, B., Di Martinelly, C., Riane, F., & Pochet, Y. (2010). Scheduling an operating theatre under human resource constraints. Computers & Industrial Engineering, 58(2), 212-220.
Saadouli, H., Jerbi, B., Dammak, A., Masmoudi, L., & Bouaziz, A. (2015). A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopedic surgery department. Computers & Industrial Engineering, 80, 72-79.
Samani, M. R. G., Torabi, S. A., Hosseini-Motlagh, S. M. (2017). Integrated blood supply chain planning for disaster relief. International journal of disaster risk reduction, 27, 168-188.
Shariff, S. R., Moin, N. H., & Omar, M. (2012). Location allocation modeling for healthcare facility planning in Malaysia. Computers & Industrial Engineering, 62(4), 1000-1010.
Simpson, N.C., Hancock, P.G. (2009). Fifty years of operational research and emergency response. Journal of the Operational Research Society, 60 (2), pp. 126-139
Tranter, M. A., Gregoire, M. B., Fullam, F. A., & Lafferty, L. J. (2009). Can patient-written comments help explain patient satisfaction with food quality? Journal of the American Dietetic Association, 109(12), 2068-2072.
Vali Siar, M. M., Gholami, S., & Ramezanian, R. (2017). Multi-period and multi-resource operating room scheduling and rescheduling using a rolling horizon approach: A case study. Journal of Industrial and Systems Engineering, 10, 97-115.
Vali-Siar, M. M., Gholami, S., & Ramezanian, R. (2018). Multi-period and multi-resource operating room scheduling under uncertainty: A case study. Computers & Industrial Engineering, 126, 549-568.
Van Essen, J. T., Hans, E. W., Hurink, J. L., & Oversberg, A. (2012). Minimizing the waiting time for emergency surgery. Operations Research for Health Care, 1(2-3), 34-44..
Xiang, W., Yin, J., & Lim, G. (2015). An ant colony optimization approach for solving an operating room surgery scheduling problem. Computers & Industrial Engineering, 85, 335-345.
Zhang, W., Cao, K., Liu, S., & Huang, B. (2016). A multi-objective optimization approach for health-care facility location-allocation problems in highly developed cities such as Hong Kong. Computers, Environment and Urban Systems, 59, 220-230.