预防性维修计划的扩展框架:提出的优化模型的风险和行为分析

P. Viveros, Marco Espinoza, R. Mena, F. Kristjanpoller
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引用次数: 1

摘要

与维护计划制定相关的复杂性的显著增加使得新技术的发展能够将维护计划优化模型带到更现实的环境中。在这个意义上,先前提出的优化模型考虑了在考虑不可忽略的执行时间的维护活动的机会主义策略下使用时间窗口来形成分组方案,从而提供了在有限资源下分析场景的可能性。本文提出了一种基于维护计划优化模型中涉及的每个组件的故障概率的风险分析,该模型具有支持更多组合的组合PM活动的特殊性。此外,它还试图确定针对每个维护活动的周期性和执行时间的不同场景的优化模型的一般行为。所提出的优化模型是在混合整数线性规划(MILP)范式下制定的,其目标函数旨在最小化与所开发活动的执行时间相关的系统不可用性,生成不同的实验案例,并在提前或延迟的容差因子从0%到最大25%的范围内改变启动时间调度。结果表明,与基本优化模型相比,当容差系数为10%时,不可用性降低8%。最后,有可能量化每个维护计划中存在的风险,同时,在延迟上提出的优化模型证明了推进PM活动的行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extended Framework for Preventive Maintenance Planning: Risk and Behaviour Analysis of a Proposed Optimization Model
The considerable increase in the complexity associated with the formulation of maintenance plans has enabled the development of new techniques to bring maintenance scheduling optimization models to more realistic environments. In this sense, a previous optimization model was proposed considering the use of time windows for the formation of grouping schemes under an opportunistic strategy for maintenance activities considering non-negligible execution times, thus offering the possibility of analysing scenarios with limited resources. This article proposes a risk analysis based on the failure probability of each component involved in the maintenance scheduling optimization model, which has the particularity of enabling a greater number of combinations of grouped PM activities. Moreover, it seeks to identify the general behaviour of the optimization model against different scenarios of periodicities and execution times of each maintenance activity. The proposed optimization model is formulated under a mixed integer linear programming (MILP) paradigm and its objective function seeks to minimize the unavailability of the system associated with the execution times of the activities developed, generating different experimental cases, and varying the start time scheduling under a tolerance factor from 0% up to a maximum of 25% for advance or delay. Results show in contrast with the base optimization model, an 8% less unavailability when the tolerance factor is 10%. Finally, it was possible to quantify the risk present in each maintenance schedule, at the same time a behaviour towards advancing PM activities is evidenced by the optimization model proposed over the delay.
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CiteScore
2.80
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