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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Joint optimization of lot-sizing and maintenance policy for a partially observable two-unit system
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Joint optimization of lot-sizing and maintenance policy for a partially observable two-unit system

机译:联合优化部分可观察的两单元系统的批量和维护策略

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摘要

In this paper, we present a new model to find the jointly optimal economic manufacturing quantity (EMQ) and preventive maintenance (PM) policy for a complex production facility. Unlike the previous joint models which dealt with EMQ and maintenance policy considering a single unit production facility and traditional maintenance approaches, we consider a production facility which consists of two modules with economic dependence. The more expensive module (unit 1) is subject to condition monitoring (CM), and only the age information of the second module (unit 2) is available, which follows a general distribution. The deterioration process of unit 1 is modeled as a continuous time hidden-Markov process. CM data is available at the end of each production run, and it provides only partial information about the hidden state of unit 1. The failure state of unit 1 is observable at any time. The objective is to develop a jointly optimal lot sizing and maintenance policy for a two-unit production facility using multivariate Bayesian control approach by minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the semi-Markov decision process (SMDP) framework. Also, a formula for the mean residual life (MRL) of the production facility is derived, which is an important statistic for practical applications. A practical example of the wind turbine CM and maintenance is provided and a comparison with other policies shows an outstanding performance of the new model and the control policy proposed in this paper.
机译:在本文中,我们提出了一种新模型,用于为复杂的生产设施找到共同的最优经济制造数量(EMQ)和预防性维护(PM)策略。与以前考虑单个设备生产设施和传统维护方法的EMQ和维护策略的联合模型不同,我们考虑的生产设施由两个具有经济依赖性的模块组成。较昂贵的模块(单元1)要进行状态监视(CM),并且只有第二模块(单元2)的年龄信息可用,该信息遵循总体分布。单元1的退化过程被建模为连续时间隐马尔可夫过程。 CM数据在每次生产运行结束时都可用,并且仅提供有关单元1的隐藏状态的部分信息。随时可以观察到单元1的故障状态。目的是通过使用多元贝叶斯控制方法,通过最大限度地减少长期预期的每单位时间的平均成本,为两台设备的生产工厂制定联合最佳的批量大小和维护策略。该问题是在半马尔可夫决策过程(SMDP)框架中制定和解决的。此外,推导了生产设备的平均剩余寿命(MRL)的公式,这对于实际应用是重要的统计数据。提供了一个风力发电机CM和维护的实例,并与其他策略进行了比较,显示了新模型和本文提出的控制策略的出色性能。

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