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Optimization of Batch Centralized Health Examination Scheduling Considering Inter-Item Transition Time and Sex
Mengdi Lv1, Xiaoming Zhao2, Fangxu Sun1
1School of Health Management Shandong Medical and Pharmaceutical University (Former Binzhou Medical College) Yantai China.
Health Science Reports
|August 8, 2026
Summary
Optimizing health examination schedules using a mathematical model and genetic algorithm significantly reduces total examination time for large groups. This approach enhances efficiency and patient flow in busy medical centers.
Area of Science:
- Operations Research
- Health Systems Management
- Biomedical Informatics
Background:
- Growing health awareness increases demand for health examinations, straining hospital resources.
- Batch health examinations face challenges with high patient volume and numerous procedures, leading to process inefficiencies.
- Optimizing scheduling is crucial to manage patient flow and improve the efficiency of group health examinations.
Purpose of the Study:
- To develop and validate an optimized scheduling model for batch health examinations.
- To improve examination efficiency and reduce the total examination duration for large groups.
- To address challenges posed by large instantaneous patient flow and numerous examination items.
Main Methods:
- Developed a health examination scheduling optimization model minimizing maximum examination duration.
- Incorporated path-dependent transition times and sex-related differences into the model.
- Utilized a genetic algorithm for scheduling solutions and modified the model for precedence constraints.
- Conducted controlled experiments, model verification, case analysis, and AnyLogic simulation for validation.
Main Results:
- The optimized scheduling scheme reduced maximum examination duration by 11.10% (24.43 minutes).
- Model verification confirmed correct implementation of scheduling logic with zero differences in examination and transfer times.
- Sensitivity analysis demonstrated the model's stability, with most service duration variations absorbed by system buffers.
Conclusions:
- The proposed mathematical programming model and genetic algorithm significantly enhance scheduling efficiency for batch health examinations.
- The approach substantially shortens overall examination duration, offering practical value for healthcare providers.
- This optimization strategy effectively manages patient flow and resource allocation in high-demand health examination settings.
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