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Case Study on Streamlining Surgical Operations: A Mathematical Approach to Operating Room Scheduling and Surgeon Allocation

Received: 28 October 2025     Accepted: 7 November 2025     Published: 8 December 2025
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Abstract

Operating rooms (ORs) represent one of the most critical and resource-intensive cost centers in any hospital. Their efficient management is paramount to both financial stability and patient care quality. Inefficient OR scheduling often leads to significant issues, including high operational costs, staff burnout due to overtime, and increased patient wait times. The main objective of this study is to develop and present a model aimed at increasing the strategic utilization of ORs, with the dual goals of minimizing associated operational costs and achieving significant cost savings. The research is grounded in a practical case study at the ABC Hospital. A primary contribution of this work is the meticulous collection and analysis of a comprehensive dataset from ABC Hospital, detailing the specific duration data for a wide variety of surgery types. This empirical data forms the foundation for our modeling approach. The approach itself centers on an optimization framework designed to address several key challenges simultaneously. The model is focused on minimizing the combined costs of regular OR opening hours and all associated overtime costs. Beyond cost, the study analyzes the complex task of sequencing surgeries within each OR to maximize throughput and efficiency. A crucial component of this analysis is the development of a robust system for assigning specific surgeries to the respective surgeons, aligning surgeon availability and specialization with the surgical schedule. By addressing the dual problems of cost minimization and procedural scheduling, this study provides a practical, data-driven framework. The anticipated outcomes include actionable recommendations for ABC Hospital that could lead to significant cost reductions, enhanced resource allocation, and a more predictable and efficient surgical environment for both patients and staff.

Published in Applied and Computational Mathematics (Volume 14, Issue 6)
DOI 10.11648/j.acm.20251406.14
Page(s) 349-355
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Operations Research, Scheduling Problem, Linear Programming Problem, Decision-making

References
[1] Testi A, Tanfani E, Torre G. A three-phase approach for operating theatre schedules. Health Care Management Science, 2007; 10(2): 163-172.
[2] Li L, Luo Y, Yang Y, et al. A MIP model for rolling horizon surgery scheduling. Journal of Medical Systems, 2016; 40(5): 1-7.194.
[3] Xiang W, Li C. Surgery scheduling optimization considering real life constraints and comprehensive operation cost of operating room. Technology & Health Care Official Journal of the European Society for Engineering & Medicine, 2014. 2015; 23(5): 605-17.
[4] Santibáñez P, Begen M, Atkins D. Surgical block scheduling in a system of hospitals: an application to resource and wait list management in a British Columbia health authority. Health Care Management Science, 2007; 10(10): 269-82.
[5] Blake JT, Donald J. Mount Sinai Hospital uses integer programming to allocate operating room time. Interfaces, 2002; 200 32(2): 63-73.
[6] Beliën J, Demeulemeester E. A branch-and-price approach for integrating nurse and surgery scheduling. European Journal of Operational Research, 2008; 189(3): 652-668.
[7] Saadouli H, Jerbi B, Dammak A, et al., A stochastic optimization and simulation approach for scheduling operating rooms and recovery beds in an orthopaedic surgery department. Computers & Industrial Engineering, 2015; 80: 72-79.
[8] Min D, Yih Y. Scheduling elective surgery under uncertainty and downstream capacity constraints. European Journal of Operational Research, 2010; 206(3): 642-652.
[9] Lamiri M, Xie X, Dolgui A, et al. A stochastic model for operating room planning with elective and emergency demand for surgery. European Journal of Operational Research, 2008; 185(3): 1026-1037.
[10] Wang Y, Tang J, Fung RYK. A column-generation-based heuristic algorithm for solving operating theater planning problem under stochastic demand and surgery cancellation risk. International Journal of Production Economics, 2014; 158: 28-36.
[11] M’Hallah R, Al-Roomi AH. The planning and scheduling of operating rooms: A simulation approach. Computers & Industrial Engineering, 2014; 78: 235-248.
[12] Persson M, Persson JA. Health economic modeling to support surgery management at a Swedish hospital. Omega, 2009; 37(4): 853-863.
[13] Addis B, et al. Operating room scheduling and rescheduling: a rolling horizon approach. Flexible Services & Manufacturing Journal, 2015; 28(1-2): 1-27.
[14] Devin AG. An application of stochastic programming method for nurse scheduling problem in real word hospital. Computers & Industrial Engineering, 2016; 96: 192-200.
[15] Mancilla C, Storer R. A sample average approximation approach to stochastic appointment sequencing and scheduling. IIE Transactions, 2012; 44(8): 655-670.
[16] Kim S, Pasupathy R, Henderson SG. A Guide to Sample Average Approximation. Handbook of Simulation Optimization. Springer New York, 2015: 207-243.
Cite This Article
  • APA Style

    Christinal, A. N. J., Jiji, D. S. (2025). Case Study on Streamlining Surgical Operations: A Mathematical Approach to Operating Room Scheduling and Surgeon Allocation. Applied and Computational Mathematics, 14(6), 349-355. https://doi.org/10.11648/j.acm.20251406.14

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    ACS Style

    Christinal, A. N. J.; Jiji, D. S. Case Study on Streamlining Surgical Operations: A Mathematical Approach to Operating Room Scheduling and Surgeon Allocation. Appl. Comput. Math. 2025, 14(6), 349-355. doi: 10.11648/j.acm.20251406.14

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    AMA Style

    Christinal ANJ, Jiji DS. Case Study on Streamlining Surgical Operations: A Mathematical Approach to Operating Room Scheduling and Surgeon Allocation. Appl Comput Math. 2025;14(6):349-355. doi: 10.11648/j.acm.20251406.14

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  • @article{10.11648/j.acm.20251406.14,
      author = {Arivudai Nambi Jenifer Christinal and Dhasaiyan Surjith Jiji},
      title = {Case Study on Streamlining Surgical Operations: 
    A Mathematical Approach to Operating Room Scheduling and Surgeon Allocation},
      journal = {Applied and Computational Mathematics},
      volume = {14},
      number = {6},
      pages = {349-355},
      doi = {10.11648/j.acm.20251406.14},
      url = {https://doi.org/10.11648/j.acm.20251406.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20251406.14},
      abstract = {Operating rooms (ORs) represent one of the most critical and resource-intensive cost centers in any hospital. Their efficient management is paramount to both financial stability and patient care quality. Inefficient OR scheduling often leads to significant issues, including high operational costs, staff burnout due to overtime, and increased patient wait times. The main objective of this study is to develop and present a model aimed at increasing the strategic utilization of ORs, with the dual goals of minimizing associated operational costs and achieving significant cost savings. The research is grounded in a practical case study at the ABC Hospital. A primary contribution of this work is the meticulous collection and analysis of a comprehensive dataset from ABC Hospital, detailing the specific duration data for a wide variety of surgery types. This empirical data forms the foundation for our modeling approach. The approach itself centers on an optimization framework designed to address several key challenges simultaneously. The model is focused on minimizing the combined costs of regular OR opening hours and all associated overtime costs. Beyond cost, the study analyzes the complex task of sequencing surgeries within each OR to maximize throughput and efficiency. A crucial component of this analysis is the development of a robust system for assigning specific surgeries to the respective surgeons, aligning surgeon availability and specialization with the surgical schedule. By addressing the dual problems of cost minimization and procedural scheduling, this study provides a practical, data-driven framework. The anticipated outcomes include actionable recommendations for ABC Hospital that could lead to significant cost reductions, enhanced resource allocation, and a more predictable and efficient surgical environment for both patients and staff.},
     year = {2025}
    }
    

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