TY - JOUR
T1 - An integrated approach for enhancing operating room management
T2 - capacity planning, fair scheduling, and surgeon resilience
AU - Kayvanfar, Vahid
AU - Baldacci, Roberto
AU - Govindan, Kannan
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/7
Y1 - 2025/7
N2 - In the realm of operating room management (ORM), several key challenges loom large, including the reduction of overtime and idle time for surgeons, avoiding overutilization and underutilization of ORs, and the equitable allocation of patients to surgeons to mitigate workload pressures. Moreover, the potential disruption risks associated with surgeons can lead to surgery cancellations. This research presents a comprehensive framework for addressing these challenges through a unified approach encompassing capacity planning and equitable scheduling within surgery departments. An essential component of this approach involves the identification of backup surgeons to minimize the surgery cancellations risk attributable to surgeon unavailability. The capacity planning phase is executed in the initial stage, leveraging Markovian queueing systems to optimize resource allocation. In the second stage, a resilient scheduling model is introduced, considering the equitable assignment of patients to surgeons. This scheduling model is developed using a goal programming approach and is solved efficiently using a novel decomposition-based heuristic method designed to expedite the optimal solution attainment. To validate the efficacy of this innovative approach, a real-world case study is undertaken, showcasing its practical application and demonstrating its proficiency in addressing the complex challenges inherent in ORM, followed by some valuable managerial insights. By implementing the proposed approach, the optimal number of operating rooms is obtained from the preceding queueing model in the capacity planning phase, and the effect of changing surgeon numbers is analyzed. Results show that optimizing unit capacity improves surgery scheduling and demonstrates the proposed framework’s efficiency.
AB - In the realm of operating room management (ORM), several key challenges loom large, including the reduction of overtime and idle time for surgeons, avoiding overutilization and underutilization of ORs, and the equitable allocation of patients to surgeons to mitigate workload pressures. Moreover, the potential disruption risks associated with surgeons can lead to surgery cancellations. This research presents a comprehensive framework for addressing these challenges through a unified approach encompassing capacity planning and equitable scheduling within surgery departments. An essential component of this approach involves the identification of backup surgeons to minimize the surgery cancellations risk attributable to surgeon unavailability. The capacity planning phase is executed in the initial stage, leveraging Markovian queueing systems to optimize resource allocation. In the second stage, a resilient scheduling model is introduced, considering the equitable assignment of patients to surgeons. This scheduling model is developed using a goal programming approach and is solved efficiently using a novel decomposition-based heuristic method designed to expedite the optimal solution attainment. To validate the efficacy of this innovative approach, a real-world case study is undertaken, showcasing its practical application and demonstrating its proficiency in addressing the complex challenges inherent in ORM, followed by some valuable managerial insights. By implementing the proposed approach, the optimal number of operating rooms is obtained from the preceding queueing model in the capacity planning phase, and the effect of changing surgeon numbers is analyzed. Results show that optimizing unit capacity improves surgery scheduling and demonstrates the proposed framework’s efficiency.
KW - Capacity planning
KW - Case study
KW - Heuristic method
KW - Operating room fair scheduling
KW - Queuing systems
KW - Resilient
UR - https://www.scopus.com/pages/publications/105005779790
U2 - 10.1007/s10479-025-06565-0
DO - 10.1007/s10479-025-06565-0
M3 - Article
AN - SCOPUS:105005779790
SN - 0254-5330
VL - 350
SP - 1385
EP - 1412
JO - Annals of Operations Research
JF - Annals of Operations Research
IS - 3
ER -