Outpatient Scheduling with Genetic Algorithm: The Power of Mutation Operators

Area of Science:

• Operations Research
• Computer Science
• Healthcare Management

Background:

• Outpatient scheduling presents significant complexity and time demands.
• Optimization methods, including genetic algorithms, have been explored to address these challenges.

Purpose of the Study:

• Develop a task-specific genetic algorithm for outpatient scheduling.
• Evaluate the impact of various mutation operators on algorithm performance.
• Minimize the earliest completion time for scheduled examinations.

Main Methods:

• Designed and compared random and two heuristic mutation operators.
• Assessed operator performance across four distinct scheduling scenarios.
• Focused on optimizing examination scheduling.

Main Results:

• The exponential mutation operator demonstrated superior performance across all scenarios.
• Achieved optimal schedules in 100% of runs for simple tasks and 74.5% for complex tasks.
• Outperformed random and polynomial mutation operators significantly.

Conclusions:

• The choice of mutation operator critically affects genetic algorithm efficiency in outpatient scheduling.
• Tailoring mutation operators to the objective function substantially enhances scheduling performance.