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Optimization of Surgical Schedules at a Specialist Hospital: A Case Application

Year 2020, Ejosat Special Issue 2020 (ISMSIT), 232 - 239, 30.11.2020
https://doi.org/10.31590/ejosat.821953

Abstract

The operations management in health services has recently become extremely important since hospitals’ main objective is to provide high-quality health services to patients while reducing their costs and improving their financial assets for their survival and growth. In this respect, operating rooms are of great interest to the hospitals since they are the hospital’s largest cost and revenue center. However, despite their criticality in health services, they are generally bottleneck resources in hospitals. Therefore, increasing the efficiency of operating rooms results in improved patient satisfaction and reduction in the cost of surgical operations. At this stage, operating room scheduling is the key to accomplish these two conflicting goals at a reasonable level simultaneously. It includes the timing of each operation as well as the assignment of surgical resources to each operation such as operating room and surgical teams over a few days or a week. Operating room surgical schedules often present logistical difficulties in terms of assigning doctors to specific operating rooms. Due to a wide variety of factors such as room availability, working hours in a week, doctor preferences, and operating room capabilities, surgical scheduling can prove to be challenging. In many hospital administrators manually modify the assignments on a case-by-case basis, which makes it difficult and time-consuming when dealing with surgical schedules. In this paper, we try to implement a linear programming procedure, which transforms the operating room schedule into a working schedule that dynamically changes weekly; and can be programmed to incorporate different scenarios within the hospital-based on specific hospital parameters. With the application of a binary linear programming model on illustrative problems by using Excel Solver Add-in, we demonstrate the advantages of using such an approach in optimizing static and dynamic surgical assignments weekly in order to meet a specific goal.

References

  • Abedinia, A., Lia, W. and Yea, H. (2017). An optimization model for operating room scheduling to reduce blocking across the perioperative process. Procedia Manufacturing, 10, 60 – 70.
  • Cappanera, P., Visintin, F. and Banditori, C. (2018). Addressing conflicting stakeholders’ priorities in surgical scheduling by goal programming. Flexible Services and Manufacturing Journal, 30, 252–271. https://doi.org/10.1007/s10696-016-9255-5
  • Cardoen B.,Demeulemeester, E. and Belien. (2009). Optimizing a multiple objective surgical case sequencing problem. Int. J.Production Economics, 119, 354–366. Gecici, E. and Guler, M. G., (2019). A decision support system for nurse scheduling problem. Pamukkale University Journal of Engineering Sciences, XX(X), XX-XX, 20XX, doi: 10.5505/pajes.2019.86402
  • Huang, G. X., Xiang, W., Li C., Zheng, Q., Zhou, S., Shen, B. Q. and Chen, S. F. (2012). Surgical Scheduling Based on Hybrid Flow-Shop Scheduling. Applied Mechanics and Materials, 201-202, 1004-1007. https://doi.org/10.4028/www.scientific.net/AMM.201-202.1004
  • Khaniyev, T., Kayis, E. and Gullu, R. (2020). Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics. European Journal of Operational Research, 286, 49–62.
  • Oostrum, J. M. V., Bredenhoff E. and Hans, E. W. (2010). Suitability and managerial implications of a Master Surgical Scheduling approach. Annals of Operations Research, 178, 91–104.
  • Silva, T. A.O. and Souza, M C. D. (2020). Surgical scheduling under uncertainty by approximate dynamic programming. Omega, 95, 102066.
  • Trilling, L., Guinet, A. and Magny, D. L. (2006). Nurse scheduling using integer linear programming and constraint programming. IFAC Proceedings Volumes, 3, 671-676.
  • Yang, Y., Shen, B., Gao, W. and Zhong, L. (2015). A surgical scheduling method considering surgeons’ preferences. Journal of Combinatorial Optimization, 30, 1016–1026. https://doi.org/10.1007/s10878-015-9853-2
  • Zhang, J., Dridi, M. and Moudni, A. E. (2020). Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints. Int. J. Production Economics, 229, 107764. https://doi.org/10.1016/j.ijpe.2020.107764

Bir İhtisas Hastanesinde Ameliyathane Programının Optimizasyonu: Bir Vaka Uygulaması

Year 2020, Ejosat Special Issue 2020 (ISMSIT), 232 - 239, 30.11.2020
https://doi.org/10.31590/ejosat.821953

Abstract

Hastaneler yüksek kaliteli sağlık hizmeti sağlarken varlıklarını sürdürebilmek ve büyüyebilmek için maliyet azaltmayı ve finansal varlıklarını artırmayı hedeflediğinden sağlık hizmetlerinde operasyon yönetimi son dönemde büyük önem kazanmıştır. Bu bağlamda, ameliyathaneler hastanelerdeki en önemli gelir ve maliyet merkezi olmaları itibariyle hastane yöneticilerinin ilgi odağı haline gelmiştir. Sağlık hizmetlerinde kritik bir öneme sahip olmalarına ragmen ameliyathaneler genellikle hastanelerde darboğazın oluştuğu kaynaklardır. Dolayısı ile, ameliyathane verimliliğini iyileştirmek, hasta memnuniyetini artırırken cerrahi operasyonların maliyetinde azalma ile sonuçlanacaktır. Bu noktada, ameliyathane çizelgeleme, bu iki çatışan hedefi belli bir düzeyde başarabilmenin anahtarı olarak öne çıkmaktadır. Ameliyathane çizelgeleme bir kaç gün veya bir haftalık bir periyotta gerçekleştirelecek ameliyatların zamanlarının belirlenmesini ve her bir ameliyat için gerekli olan ameliyathane ve ameliyat ekibi gibi kaynakların atanmasını kapsamaktadır. Ameliyathane çizelgeleme sürecinde doktorların belirli ameliyathanelere atanması esanasında genellikle bir takım lojistik zorluklar ortaya çıkar. Bu nedenle, ameliyathane çizelgeleme, ameliyathane kullanılabilirliği, çalışma saatleri, doctor tercihleri ve ameliyathanelerin teknolojik yeterliliği gibi çeşitli faktörler nedeniyle oldukça zorlayıcı bir iş haline dönüşebilir. Pek çok hastanede, idariciler ameliyathane atamalarını vakalar bazında manuel şekilde değerlendirerek değiştirmekte; bu da çizelgelemede zorluklara ve zaman kaybına yol açmaktadır. Bu çalışmada, spesifik hastane parametlerini göz önünde bulundurarak farklı senaryoları dahil edebileceğimiz bir esnekliğe sahip bir doğrusal programalama modeli geliştirerek haftalık değişen ameliyat gereksinimlerini dinamik bir ameliyathane çizelgesine dönüştüren bir yöntem uygulayacağız. Model detaylı bir şekilde anlatıldıktan sonra geliştirilen ikili doğrusal programlama yaklaşımının örnek problemler üzerinde Excel Çözücü eklentisi kullanılarak uygulanması ile ameliyat için gerekli olan kaynakların haftalık bazda atanması statik ve dinamik durumlar için optimize edilmiş; belirli bir hedef açısından ortaya çıkan avantajlar ortaya konmuştur.

References

  • Abedinia, A., Lia, W. and Yea, H. (2017). An optimization model for operating room scheduling to reduce blocking across the perioperative process. Procedia Manufacturing, 10, 60 – 70.
  • Cappanera, P., Visintin, F. and Banditori, C. (2018). Addressing conflicting stakeholders’ priorities in surgical scheduling by goal programming. Flexible Services and Manufacturing Journal, 30, 252–271. https://doi.org/10.1007/s10696-016-9255-5
  • Cardoen B.,Demeulemeester, E. and Belien. (2009). Optimizing a multiple objective surgical case sequencing problem. Int. J.Production Economics, 119, 354–366. Gecici, E. and Guler, M. G., (2019). A decision support system for nurse scheduling problem. Pamukkale University Journal of Engineering Sciences, XX(X), XX-XX, 20XX, doi: 10.5505/pajes.2019.86402
  • Huang, G. X., Xiang, W., Li C., Zheng, Q., Zhou, S., Shen, B. Q. and Chen, S. F. (2012). Surgical Scheduling Based on Hybrid Flow-Shop Scheduling. Applied Mechanics and Materials, 201-202, 1004-1007. https://doi.org/10.4028/www.scientific.net/AMM.201-202.1004
  • Khaniyev, T., Kayis, E. and Gullu, R. (2020). Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics. European Journal of Operational Research, 286, 49–62.
  • Oostrum, J. M. V., Bredenhoff E. and Hans, E. W. (2010). Suitability and managerial implications of a Master Surgical Scheduling approach. Annals of Operations Research, 178, 91–104.
  • Silva, T. A.O. and Souza, M C. D. (2020). Surgical scheduling under uncertainty by approximate dynamic programming. Omega, 95, 102066.
  • Trilling, L., Guinet, A. and Magny, D. L. (2006). Nurse scheduling using integer linear programming and constraint programming. IFAC Proceedings Volumes, 3, 671-676.
  • Yang, Y., Shen, B., Gao, W. and Zhong, L. (2015). A surgical scheduling method considering surgeons’ preferences. Journal of Combinatorial Optimization, 30, 1016–1026. https://doi.org/10.1007/s10878-015-9853-2
  • Zhang, J., Dridi, M. and Moudni, A. E. (2020). Column-generation-based heuristic approaches to stochastic surgery scheduling with downstream capacity constraints. Int. J. Production Economics, 229, 107764. https://doi.org/10.1016/j.ijpe.2020.107764
There are 10 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Mehmet Savsar 0000-0003-4299-8545

Muhammet Enis Bulak 0000-0003-3784-7830

Orkun Kozanoğlu 0000-0003-1006-4879

Publication Date November 30, 2020
Published in Issue Year 2020 Ejosat Special Issue 2020 (ISMSIT)

Cite

APA Savsar, M., Bulak, M. E., & Kozanoğlu, O. (2020). Optimization of Surgical Schedules at a Specialist Hospital: A Case Application. Avrupa Bilim Ve Teknoloji Dergisi232-239. https://doi.org/10.31590/ejosat.821953