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Dynamic Surgery Scheduling Based on an Improved Genetic Algorithm.

Bingbing Zhang¹, Qiang Su¹

¹School of Economics and Management, Tongji University, Shanghai 200092, China.

Journal of Healthcare Engineering

|December 2, 2021

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This study introduces a new stochastic programming model and an improved genetic algorithm (IGA) to optimize elective surgery scheduling. The IGA enhances resource utilization by minimizing surgeon waiting and operating room idle times.

Area of Science:

Operations Research
Healthcare Management
Applied Mathematics

Background:

Dynamic scheduling of elective surgeries presents challenges due to uncertain surgery durations.
Existing scheduling methods may not fully optimize resource allocation in operating rooms.

Purpose of the Study:

To develop a stochastic programming formulation for dynamic surgical scheduling.
To propose an efficient improved genetic algorithm (IGA) for solving this complex problem.

Main Methods:

Formulation of a novel stochastic programming model for dynamic scheduling.
Implementation of an improved genetic algorithm (IGA) to find optimal solutions.
Numerical analysis to evaluate the effectiveness of the proposed method.

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Main Results:

The proposed IGA effectively addresses the dynamic scheduling problem.
Dynamic scheduling using IGA significantly improves resource utilization.
Reductions in surgeon waiting time and operating room idle time were observed.

Conclusions:

The stochastic programming formulation and IGA provide an effective approach for dynamic elective surgery scheduling.
The IGA-based solution enhances operational efficiency in surgical settings.
This method offers a valuable tool for optimizing healthcare resource management.