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Genetic Algorithm for Solving the No-Wait Three-Stage Surgery Scheduling Problem.

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  • 1Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 407102, Taiwan.

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Summary
This summary is machine-generated.

This study optimizes elective surgery scheduling using a genetic algorithm (GA) to minimize patient treatment time. The GA efficiently finds near-optimal solutions for the three-stage operating room scheduling problem.

Keywords:
genetic algorithmmakespanoperating roomsschedulingsurgery

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Area of Science:

  • Operations Research
  • Healthcare Management
  • Computer Science

Background:

  • Operating room scheduling is complex, involving multiple stages and resources.
  • Minimizing makespan improves operating room utilization and patient satisfaction.
  • Elective surgeries require efficient scheduling for timely patient care.

Purpose of the Study:

  • To develop and evaluate a genetic algorithm (GA) for a deterministic three-stage operating room surgery scheduling problem.
  • To minimize the makespan, defined as the maximum end time of the last activity in post-surgery.
  • To assess the efficiency and effectiveness of the proposed GA in finding near-optimal solutions.

Main Methods:

  • A deterministic three-stage operating room scheduling model was formulated.
  • A genetic algorithm (GA) was designed and implemented to solve the scheduling problem.
  • The GA's performance was evaluated using randomly generated problem instances.

Main Results:

  • The proposed genetic algorithm (GA) demonstrated efficiency in solving the operating room scheduling problem.
  • On average, the GA deviated by 3.25% from the calculated lower bound (LB).
  • The average computation time for the GA was 10.71 seconds.

Conclusions:

  • The genetic algorithm (GA) is an effective tool for optimizing daily three-stage operating room surgery scheduling.
  • The GA provides near-optimal solutions with acceptable computational time.
  • Efficient scheduling enhances operating room utilization and patient care delivery.