Abstract

The costs of preventive maintenance have been extensively studied by scholars across all preventive optimization model disciplines. However, one phenomenon fails to be fully studied: breakdown and breakdown maintenance costs. We set out to fill this gap in this study. This study considered the cost of equipment preventive maintenance, and the breakdown maintenance cost caused by an accidental breakdown. In order to more accurately establish the reliability model of equipment breakdown, the three-parameter Weibull distribution was applied to set up the reliability model of equipment and the differential evolution algorithm was adopted to optimize the parameters. On this basis, preventive maintenance was regarded as imperfect maintenance in the study of preventive maintenance strategies for single equipment. In consideration of the combined influence of preventive maintenance and breakdown maintenance, a maintenance strategy in which preventive maintenance times N served as the decision variable was obtained to build a mathematical model on benefit expectation of single equipment in unit time. Based on the research of single equipment, a further study was performed on the multi-equipment series system. Moreover, two strategies were given, both of which use preventive maintenance times N as the decision variable. The first strategy is to take the average cost rate of the system under long-term operation as the optimization objective, while the second strategy is to apply single component maintenance strategy in series system. In the numerical example study, a series manufacturing system composed of two devices was chosen as the research object. Interestingly, we discussed the effect of initial conditions of two-parameter DE on output results. After acquiring the optimal initialization parameters, the failure rate function was achieved by the DE to estimate the parameters of three-parameter Weibull distribution. Meanwhile, the maintenance times N was optimized according to the two strategies respectively. The best result were selected from the results of both strategies based on availability. Thus, the validity and practicability of the proposed research methods are verified.

中文翻译：

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基于差分进化算法的批量生产系统预防性维修优化。

摘要

学者已在所有预防性优化模型学科中广泛研究了预防性维护的成本。但是，一个现象未能得到充分研究：故障和故障维护成本。我们着手填补这项研究中的空白。这项研究考虑了设备预防性维护的成本，以及由于意外故障而导致的故障维护成本。为了更准确地建立设备故障的可靠性模型，采用三参数威布尔分布建立设备可靠性模型，并采用微分进化算法对参数进行优化。在此基础上，预防性维护被认为是单设备预防性维护策略中不完善的维护。获得N作为决策变量，以建立单位时间内单个设备的收益期望的数学模型。在单设备研究的基础上，对多设备系列系统进行了进一步的研究。此外，给出了两种策略，均使用预防性维护时间N作为决策变量。第一种策略是将长期运行下系统的平均成本率作为优化目标，第二种策略是将单组件维护策略应用于串联系统。在数值实例研究中，选择了由两个设备组成的系列制造系统作为研究对象。有趣的是，我们讨论了两参数DE的初始条件对输出结果的影响。在获得最优的初始化参数后，通过DE实现故障率函数，以估计三参数威布尔分布的参数。同时，维护时间N分别根据这两种策略进行了优化。根据可用性从两种策略的结果中选择最佳结果。因此，验证了所提出的研究方法的有效性和实用性。