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3681. Advancing elective surgery scheduling considering operating theatre and non-operating theatre resources with time and demand uncertainties
Invited abstract in session TB-10: Surgery Scheduling and Operating Room Planning, stream OR in Health Services (ORAHS).
Tuesday, 10:30-12:00Room: 11 (building: 116)
Authors (first author is the speaker)
| 1. | Mona Koushan
|
| Management, Marketing & Tourism, University of Canterbury | |
| 2. | Lincoln Wood
|
| Department of Management, University of Otago |
Abstract
The anticipated surge in demand for elective surgical services highlights the urgency of optimising hospital resources, especially in publicly funded systems with limited budgets. Effective coordination of materials and resources under operating theatre (OT) capacity management is essential to meet the rising volume of elective surgeries. This study introduces a novel multi-stage care facility model, emphasising the significance of both OT and non-OT processes, aiming to balance financial constraints with community satisfaction. It explores enhancing OT scheduling using the theory of variation and uncertainty buffering, incorporating a time buffer into the surgical process. Furthermore, the study addresses uncertainties related to emergency arrivals and processing time, employing robust optimisation to ensure adequate capacity at each stage of the surgery process, thereby enhancing the model's relevance and realism. Solving this model, the study proposes meta-heuristic algorithms like Hybrid Nondominated Sorting Genetic Algorithm and hybrid Multi-Objective Firefly Optimisation algorithms with Hybrid Non-dominated Sorting Genetic Algorithm to efficiently address real-world scale scheduling problems. These meta-heuristics facilitate rapid adjustments in schedules in response to evolving circumstances, making them valuable tools for healthcare practice. This study enhances surgical service delivery and resource management in hospitals by advancing understanding of OT scheduling.
Keywords
- Health Care
- Scheduling
- Robust Optimization
Status: accepted
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