医院消毒流程的两阶段混合流程-车间配方

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sebastian Kraul
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引用次数: 0

摘要

无菌处理是一个关键的辅助流程,也是昂贵医疗器械的加工、购置和储存过程中的主要成本因素。本文旨在通过开发、实施和评估一种基于调度规则的算法来提高无菌处理的性能,该算法采用两阶段混合流-shop公式来减少医疗器械在中央无菌供应部门的停留时间。该算法将调度规则与阶段分解和兼容性条件相结合。除分析约束外,还设计了一种遗传算法来确定性能基准。来自德国一家大型医院的真实数据被用来测试启发式算法的有效性。案例研究证明了该方法的实际意义,它缩短了医疗设备在系统中的停留时间,提高了清洗消毒机和灭菌器的利用率。它还强调了根据需求调整机器容量的重要性,以及与批量处理决策相关的潜在权衡。我们的方法有助于节省大量运营成本并提高效率,为运营和战术层面的决策者带来巨大收益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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 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.
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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