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

Yang-Kuei Lin¹, Chen-Hao Yen¹

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

Healthcare (Basel, Switzerland)

|March 11, 2023

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 algorithm makespan operating rooms scheduling surgery

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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.