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Emergency Undocking in Robotic Surgery: A Simulation Curriculum
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A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints.

Wei Xiang1, Jiao Yin1, Gino Lim2

  • 1Faculty of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, PR China.

Artificial Intelligence in Medicine
|January 8, 2015
PubMed
Summary

This study introduces an Ant Colony Optimization (ACO) approach to optimize operating room (OR) surgery scheduling by integrating nurse roster constraints. The ACO method significantly reduces surgery end times and nurse overtime, improving hospital resource utilization.

Keywords:
Ant colony optimizationNurse rosterOperating room managementSurgery scheduling

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Artificial Intelligence in Medicine

Background:

  • Operating room (OR) surgery scheduling is critical for patient treatment and hospital resource management.
  • Existing scheduling methods often overlook complex real-life nurse roster constraints, including role, specialty, qualification, and availability.
  • This gap necessitates advanced optimization techniques to improve efficiency and resource allocation in surgical scheduling.

Purpose of the Study:

  • To develop and evaluate a novel mathematical model and an Ant Colony Optimization (ACO) approach for OR surgery scheduling.
  • To integrate real-life nurse roster constraints into the surgery scheduling problem.
  • To improve patient treatment timeliness and hospital resource utilization through optimized scheduling.

Main Methods:

  • A modified Ant Colony Optimization (ACO) algorithm featuring a two-level ant graph model (surgeries and dynamic resources) was developed.
  • Three pheromone types (sequence, surgery, and resource-related) were defined for the two-level model.
  • The algorithm employed an iteration-best and feasible update strategy with local pheromone updates to prioritize optimal makespan and balanced resource utilization.

Main Results:

  • The ACO approach demonstrated a 1.5-hour reduction in surgery end time compared to literature benchmarks.
  • Significant reductions in resource working time variation were observed: 25% for ORs, 50% for nurses (shift 1), and 86% for nurses (shift 2).
  • Total nurse overtime was reduced by 42%, with a 0.25-hour decrease in individual maximum overtime.
  • Real-world hospital data showed ACO shortened end times by 7.4%–24.6% (95% confidence interval) through more balanced surgery assignments.

Conclusions:

  • The proposed ACO approach effectively solves the surgery scheduling problem, incorporating daily nurse roster constraints.
  • The method achieves shortened surgery end times and more balanced resource allocations, enhancing operational efficiency.
  • Integrating surgery and nurse scheduling systematically optimizes the complete three-stage surgery flow and involved resources.