A two-stage hybrid flow-shop formulation for sterilization processes in hospitals
Sterile processing is a critical secondary process and a major cost factor in the processing, acquisition, and storage of costly medical devices. This article aims to improve the performance of sterile processing by developing, implementing, and evaluating a dispatching rule-based algorithm to reduc...
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Veröffentlicht in: | Expert systems with applications 2025-03, Vol.262, p.125624, Article 125624 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Sterile processing is a critical secondary process and a major cost factor in the processing, acquisition, and storage of costly medical devices. This article aims to improve the performance of sterile processing by developing, implementing, and evaluating a dispatching rule-based algorithm to reduce the time medical devices spend in the central sterile supply department using a two-stage hybrid flow-shop formulation. The algorithm combines dispatching rules with stage decomposition and compatibility conditions. A genetic algorithm is designed to benchmark the performance in addition to an analytic bound. Real-world data from a large German hospital were used to test the effectiveness of the heuristics. The case study demonstrated the practical implications of the approach, leading to a reduction in the time medical devices spend in the system and improved utilization of washer-disinfector machines and sterilizers. It also highlighted the importance of aligning machine capacity with demand and the potential trade-offs associated with batch processing decisions. Our approach can contribute to substantial operational cost savings and efficiency gains, offering significant benefits to decision makers at both the operational and tactical levels.
•Improved sterile processing with a dispatching rule-based algorithm.•Reduced device time in central sterile supply using a two-stage hybrid flow-shop.•Designed a genetic algorithm to benchmark performance and analytic bounds.•Tested heuristics using real-world data from a large German hospital. |
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ISSN: | 0957-4174 |
DOI: | 10.1016/j.eswa.2024.125624 |