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Updated: Feb 5, 2026
09:41
A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
Published on: May 20, 2016
12.7K
Multi-objective integrated planning and scheduling model for operating rooms under uncertainty
Javad Ansarifar1, Reza Tavakkoli-Moghaddam1,2, Faezeh Akhavizadegan1
11 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Summary
This study optimizes operating room scheduling by integrating real-world constraints and uncertainty. The developed model, solved using meta-heuristic algorithms, significantly improves upon manual scheduling for efficiency and resource utilization.
Area of Science:
- Operations Research
- Healthcare Management
- Applied Mathematics
Background:
- Operating room (OR) scheduling is complex, involving numerous real-world constraints.
- Existing scheduling methods often fail to account for decision-making styles, multi-stage surgeries, and resource time windows.
- Uncertainty in surgical processes impacts efficiency and resource allocation.
Purpose of the Study:
- To develop an integrated mathematical programming model for OR planning and scheduling.
- To incorporate decision-making styles, multi-stage surgeries, resource constraints, and uncertainty into the OR scheduling model.
- To optimize net revenues, minimize decision-making inconsistency, and maximize OR utilization.
Main Methods:
- Formulation of a fuzzy possibilistic-stochastic mathematical programming approach.
- Integration of multi-objective optimization considering revenue, human resource consistency, and OR utilization.
- Application of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) for solving the model.
- Validation using test problems based on data from a public hospital in Iran.
Main Results:
- NSGA-II demonstrated superior performance compared to MOPSO across multiple metrics for OR scheduling.
- The proposed model significantly outperforms manual scheduling in terms of efficiency and effectiveness.
- The model successfully balances competing objectives: maximizing revenue, ensuring consistency, and optimizing resource use.
Conclusions:
- The developed integrated model provides an effective and efficient solution for OR planning and scheduling.
- Meta-heuristic algorithms, particularly NSGA-II, are well-suited for solving complex multi-objective OR scheduling problems.
- The findings highlight the potential for significant improvements in healthcare operations through advanced mathematical modeling and optimization techniques.
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