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Optimizing operating room scheduling through multi-level learning and column generation: a novel hybrid approach.

Rong Zhao1, Yaqin Quan1, Guangrui Fan2

  • 1Department of Anesthesiology, Shanxi Provincial People Hospital, 99 Shuangta East Street, Taiyuan, Shanxi, 030001, China.

Health Care Management Science
|September 27, 2025
PubMed
Summary

This study introduces a hybrid framework for operating room (OR) scheduling, improving efficiency and patient care. The novel approach enhances OR utilization and reduces patient waiting times through advanced optimization techniques.

Keywords:
Column generationHealthcare operations managementMulti-level optimizationOperating room schedulingOperations researchReinforcement learningUncertainty handling

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Area of Science:

  • Healthcare Operations Research
  • Artificial Intelligence in Medicine
  • Hospital Management Systems

Background:

  • Operating room (OR) scheduling is vital for patient outcomes and hospital efficiency.
  • Traditional scheduling methods face challenges with complex constraints and uncertainties.
  • Optimizing OR schedules requires balancing multiple objectives and real-world variability.

Purpose of the Study:

  • To develop a novel hybrid framework for optimizing operating room (OR) scheduling.
  • To integrate multi-level optimization with reinforcement learning and column generation.
  • To address complex constraints and uncertainties in OR scheduling for improved efficiency.

Main Methods:

  • A hybrid framework decomposing the problem into strategic, tactical, and operational levels.
  • Integration of reinforcement learning to guide column generation for enhanced scheduling options.
  • Incorporation of robust uncertainty handling mechanisms for variable surgery durations and resource availability.

Main Results:

  • Reduced average patient waiting time by 15.8% (10.1 to 8.5 days).
  • Increased OR utilization by 5.4 percentage points (73.8% to 79.2%).
  • Achieved a 92.5% feasibility rate and reduced schedule disruptions by 26.2% under uncertainty.

Conclusions:

  • The hybrid framework offers a practical and scalable solution for optimizing OR scheduling.
  • Demonstrated significant improvements in healthcare delivery and operational performance in real hospital settings.
  • Provides a viable approach for sustainable improvements in hospital efficiency and patient care.