Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation · 2026
[Research on Opportunistic Maintenance Strategy for Hospital Diagnostic Equipment Considering Mixed Time Windows Based on INSGA-Ⅱ].
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Guo Chenjie, Liu Qinming
Аннотация
OBJECTIVE: To address the issues of high maintenance costs, patient care disruption due to equipment downtime, and low maintenance efficiency in large-scale hospital diagnostic equipment, this study aims to develop a maintenance strategy that is more aligned with actual clinical practices.
METHODS: First, the specific characteristics of diagnostic equipment maintenance in terms of patient safety, equipment quality control, and downtime impact are analyzed, and a single-component failure rate evolution model that accounts for equipment performance degradation is constructed. Second, a mixed time window mechanism consisting of soft and hard time windows is introduced to simulate the flexibility of clinical maintenance scheduling and the risks and costs associated with maintenance node deviations. On this basis, a multi-objective opportunistic maintenance optimization model is established, aiming to minimize preventive maintenance costs and opportunistic maintenance costs, while maximizing the improvement in effective age.
RESULTS: An improved non-dominated sorting genetic algorithm Ⅱ (INSGA-Ⅱ) is employed to solve the model. Compared with traditional methods, the proposed model can effectively reduce the frequency of equipment downtime, extend the average maintenance interval in the optimized scheme, and theoretically increase equipment availability.
CONCLUSION: The proposed model can ensure equipment reliability while balancing economic efficiency and clinical applicability, providing decision-making support for hospital medical engineering departments to formulate scientific equipment maintenance plans.
METHODS: First, the specific characteristics of diagnostic equipment maintenance in terms of patient safety, equipment quality control, and downtime impact are analyzed, and a single-component failure rate evolution model that accounts for equipment performance degradation is constructed. Second, a mixed time window mechanism consisting of soft and hard time windows is introduced to simulate the flexibility of clinical maintenance scheduling and the risks and costs associated with maintenance node deviations. On this basis, a multi-objective opportunistic maintenance optimization model is established, aiming to minimize preventive maintenance costs and opportunistic maintenance costs, while maximizing the improvement in effective age.
RESULTS: An improved non-dominated sorting genetic algorithm Ⅱ (INSGA-Ⅱ) is employed to solve the model. Compared with traditional methods, the proposed model can effectively reduce the frequency of equipment downtime, extend the average maintenance interval in the optimized scheme, and theoretically increase equipment availability.
CONCLUSION: The proposed model can ensure equipment reliability while balancing economic efficiency and clinical applicability, providing decision-making support for hospital medical engineering departments to formulate scientific equipment maintenance plans.