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Üniversite Hastanelerinde Hasta Bekleme Sürelerinin Kuyruk Teorisi ile Analizi: M/M/c ve M/G/c Modelleri

Year 2025, Volume: 40 Issue: 4, 1252 - 1286, 01.10.2025
https://doi.org/10.24988/ije.1599052

Abstract

Bu çalışma, bir üniversite hastanesinin ortopedi polikliniğinde hasta bekleme sürelerini analiz ederek hizmet süreçlerini iyileştirme ve kaynak kullanımını optimize etme amacıyla gerçekleştirilmiştir. Kuyruk teorisi modelleri olan M/M/c ve M/G/c kullanılarak sistemin mevcut performansı değerlendirilmiş ve yoğunluk artışı senaryoları incelenmiştir. M/M/c modeli, hizmet sürelerinin üstel dağıldığını varsayarak sistemin düşük yoğunluk koşullarında verimli çalıştığını göstermiştir. Kuyruk bekleme süresi 17,85 dakika, toplam sistem bekleme süresi ise 34,02 dakika olarak hesaplanmıştır. Model, mevcut doktor sayısıyla hasta taleplerinin karşılandığını ortaya koymuştur. M/G/c modeli, hizmet sürelerindeki varyasyonları dikkate alarak daha gerçekçi sonuçlar sunmuştur. Kuyruk bekleme süresi 9,06 dakika, toplam bekleme süresi ise 25,22 dakika olarak hesaplanmıştır. Ancak yoğun saatlerde hizmet süresi değişkenliği bekleme sürelerinde artışa neden olmuştur. Senaryo analizlerinde, hasta giriş oranının %10’dan %100’e kadar artırılması durumunda, her iki model de artan yoğunluğu karşılamış, ancak M/M/c modeli stabil performans sergilerken, M/G/c modeli daha hassas ama gerçekçi sonuçlar sunmuştur. %70-80 yoğunluk oranı hedeflenerek mevcut doktor sayısının genellikle yeterli olduğu belirlenmiştir. Ancak aşırı talep durumunda ek kaynak planlaması gerekebileceği vurgulanmıştır. Sonuç olarak, kuyruk teorisi modelleri, bekleme sürelerini optimize etme, hasta memnuniyetini artırma ve kaynakları etkin kullanma açısından stratejik bir araçtır. Özellikle değişken talep koşullarında M/G/c modeli daha uygun bir çözüm sunmaktadır.

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Year 2025, Volume: 40 Issue: 4, 1252 - 1286, 01.10.2025
https://doi.org/10.24988/ije.1599052

Abstract

References

  • Aeenparast, A., Tabibi, S. J., Shahanaghi, K. ve Aryanejhad, M. B. (2013). Reducing outpatient waiting time: a simulation modeling approach. Iranian Red Crescent Medical Journal, 15(9). doi: 10.5812/ircmj.7908
  • Afrane, S. ve Appah, A. (2014). Queuing theory and the management of Waiting-time in Hospitals: The case of Anglo Gold Ashanti Hospital in Ghana. International Journal of Academic Research in Business and Social Sciences, 4(2), 34-44. doi:10.6007/IJARBSS/v4-i2/590
  • Ainbinder, I., Temnikov, E. ve Allalouf, M. (2024). A study comparing waiting times in global and local queuing systems with heterogeneous workers. Applied Sciences, 14(9), 3799. https://doi.org/10.3390/app14093799
  • Alhaider, A. A., Lau, N., Davenport, P. B. ve Morris, M. K. (2019). Command and Control for Managing Patient Flow. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 8(1), 273-274. https://doi.org/10.1177/2327857919081065
  • Amjath, M., Kerbache, L. ve Smith, J. M. (2024). A closed queueing networks approach for an optimal heterogeneous fleet size of an inter-facility bulk material transfer system. Logistics, 8(1), 26. https://doi.org/10.3390/logistics8010026
  • Azfar, S. M., Murad, M. A., Azim, S. R. ve Baig, M. (2019). Misdirected patients in orthopedic outpatient clinics: a retrospective four years data analysis (23435 patients). Cureus, 11(12), e6526. DOI: 10.7759/cureus.6526
  • Bahadori, M., Mohammadnejhad, S. M., Ravangard, R. ve Teymourzadeh, E. (2014). Using queuing theory and simulation model to optimize hospital pharmacy performance. Iranian Red Crescent Medical Journal, 16(3). doi: 10.5812/ircmj.16807
  • El Haddad, M. ve Belarbi, F. (2016). Approximate anlysis of an unreliable M/M/c retrial queue with phase merging algorithm. New Trends in Mathematical Sciences, 4(3), 9-21. http://dx.doi.org/10.20852/ntmsci.2016318801
  • Belciug, S. ve Gorunescu, F. (2015). Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation. Journal of Biomedical Informatics, 53, 261-269. https://doi.org/10.1016/j.jbi.2014.11.010
  • Bhattacharyya, D., Rao, N. T., Srinivas, P., & Levy, J. (2019). Analysis of queuing applications performance using matlab. International Journal of Advanced Science and Technology, 127, 1-12. http://dx.doi.org/10.33832/ijast.2019.127.01
  • Bittencourt, O. N., Verter, V. ve Yalovsky, M. (2018). Hospital capacity management based on the queueing theory. International Journal of Productivity and Performance Management, 67(2), 224-238. https://doi.org/10.1108/IJPPM-12-2015-0193
  • Brown, L., N., G., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S. ve Zhao, L. (2005). Statistical Analysis of a Telephone Call Center: A Queueing-science Perspective. Journal of the American Statistical Association, 100(469), 36–50. https://doi.org/10.1198/016214504000001808
  • Bruneel, H., Fiems, D. ve Walraevens, J. (2014). Queueing Models for the Analysis of Communication Systems. TOP, 22, 421–448. https://doi.org/10.1007/s11750-014-0330-3
  • Cassandras, C. G., Lafortune, S., Cassandras, C. G. ve Lafortune, S. (2021). Introduction to Queueing Theory. Introduction to Discrete Event Systems, 465-533. https://doi.org/10.1007/978-3-030-72274-6_8
  • Cho, K. W., Kim, S. M., Chae, Y. M. ve Song, Y. U. (2017). Application of Queueing Theory to the Analysis of Changes in Outpatients' Waiting Times in Hospitals Introducing EMR. Healthcare Informatics Research, 23(1), 35-42. https://doi.org/10.4258/hir.2017.23.1.35
  • Chukwuoyims, K. E., Olovoeze, A. O., Igbokwe, C. U., Omeri, K. N., Geoffrey, U., Okeke, B. C. ve Anyalor, M. C. (2024). Assessıng Waıtıng Tıme Management Strategıes And Patıent Satısfactıon At Mater Mıscerıcordıae Afıkpo, AE-FUNAI JOURNAL OF ACCOUNTING, BUSINESS & FINANCE, 2024, Vol. 9(1), 289 -301
  • Creemers, S. ve Lambrecht, M. (2007). Modeling a healthcare system as a queueing network: the case of a Belgian hospital. Available at SSRN 1093618. http://dx.doi.org/10.2139/ssrn.1093618
  • Defraeye, M. ve Van Nieuwenhuyse, I. (2016). Staffing and Scheduling Under Nonstationary Demand for Service: A Literature Review. Omega, 58, 4–25. https://doi.org/10.1016/j.omega.2015.04.002
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  • Dilrukshi, P. A., Nirmanamali, H. D., Lanef, G. H. ve Samarakoon, M. A. (2016). A Strategy to Reduce the Waiting Time at the Outpatient Department of the National Hospital in Sri Lanka. International Journal o f Scientific and Research Publications, 6(2), 281-287.
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Queuing Theory Analysis of Patient Waiting Times in University Hospitals: M/M/c and M/G/c Models

Year 2025, Volume: 40 Issue: 4, 1252 - 1286, 01.10.2025
https://doi.org/10.24988/ije.1599052

Abstract

This study was conducted to improve service processes and optimize resource utilization by analyzing patient waiting times in the orthopedics outpatient clinic of a university hospital. Using the queuing theory models M/M/c and M/G/c, the system’s current performance was assessed, and scenarios simulating increased service demand were examined. The M/M/c model showed that the system operates efficiently under low-density conditions. Assuming exponential distribution of service times. The queue waiting time was calculated as 17.85 minutes, and total system waiting time as 34.02 minutes. The model revealed that patient demands were met with the number of doctors available. The M/G/c model provided more realistic results by taking into account variations in service times. Queue waiting time was calculated as 9.06 minutes and total waiting time as 25.22 minutes. However, service time variability during peak hours caused an increase in waiting times. In the scenario analyses, when the patient arrival rate was increased from 10% to 100%, both models handled the increased load. However, while the M/M/c model maintained stable performance, the M/G/c model yielded more accurate and realistic results. Targeting a 70-80% density rate, it was determined that the number of available doctors was generally sufficient. However, it was emphasized that additional resource planning may be required in case of excessive demand. In conclusion, queuing theory models are a strategic tool for optimizing waiting times, increasing patient satisfaction and using resources efficiently. Especially under variable demand conditions, the M/G/c model offers a more appropriate solution.

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Details

Primary Language Turkish
Subjects Business Administration
Journal Section Articles
Authors

Alkan Durmuş 0000-0002-5806-9962

Early Pub Date October 1, 2025
Publication Date October 1, 2025
Submission Date December 10, 2024
Acceptance Date May 16, 2025
Published in Issue Year 2025 Volume: 40 Issue: 4

Cite

APA Durmuş, A. (2025). Üniversite Hastanelerinde Hasta Bekleme Sürelerinin Kuyruk Teorisi ile Analizi: M/M/c ve M/G/c Modelleri. İzmir İktisat Dergisi, 40(4), 1252-1286. https://doi.org/10.24988/ije.1599052
İzmir Journal of Economics
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