Age replacement policy with lead-time for a system subject to non-homogeneous pure birth shocks

Sheu, Shey‐Huei; Zhang, Zhe George; Chien, Yu‐Hung; Huang, Tsun-Hung

doi:10.1016/j.apm.2013.03.017

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…Age replacement policies with minimal repairs for a system under minor and catastrophic failures due to nonhomogeneous pure birth shock have been surveyed by Sheu et al 70 Mercier and Pham 71 have discussed a PM policy for a continuously monitored system that gently and stochastically deteriorates based on a bivariate nondecreasing Lévy process. For a system which is exposed to shocks that arrive according to a nonhomogeneous pure birth process, Sheu et al 72 have considered an age‐based replacement strategy which replaces the system whenever its age reaches

T_{PM}

and a spare for replacement is available. Whenever a shock occurs, the system enters one of the two types of failure states.…”

Section: An Overview Of Classical Optimal Pm Modelsmentioning

confidence: 99%

An overview of some classical models and discussion of the signature‐based models of preventive maintenance

Asadi

Hashemi

2022

Appl Stoch Models Bus & Ind

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In reliability engineering literature, a large number of research papers on optimal preventive maintenance (PM) of technical systems (networks) have appeared based on preliminary many different approaches. According to the existing literature on PM strategies, the authors have considered two scenarios for the component failures of the system. The first scenario assumes that the components of the system fail due to aging, while the second scenario assumes the system fails according to the fatal shocks arriving at the system from external or internal sources. This article reviews different approaches on the optimal strategies proposed in the literature on the optimal maintenance of multi‐component coherent systems. The emphasis of the article is on PM models given in the literature whose optimization criteria (cost function and stationary availability) are developed by using the signature‐based (survival signature‐based) reliability of the system lifetime. The notions of signature and survival signature, defined for systems consisting of one type or multiple types of components, respectively, are powerful tools assessing the reliability and stochastic properties of coherent systems. After giving an overview of the research works on age‐based PM models of one‐unit systems and k‐out‐of‐n systems, we provide a more detailed review of recent results on the signature‐based and survival signature‐based PM models of complex systems. In order to illustrate the theoretical results on different proposed PM models, we examine two real examples of coherent systems both numerically and graphically.

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T_{PM}

and a spare for replacement is available. Whenever a shock occurs, the system enters one of the two types of failure states.…”

Section: An Overview Of Classical Optimal Pm Modelsmentioning

confidence: 99%

An overview of some classical models and discussion of the signature‐based models of preventive maintenance

Asadi

Hashemi

2022

Appl Stoch Models Bus & Ind

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show abstract

“…From the above literature, it is acknowledged that the spare unit ordering policy that determines when to order a spare and when to replace the original system is extremely important if the random lead time is taken into account (Wang et al., 2009; Chang, 2018). More spare ordering policies are proposed and optimized from a variety of perspectives (Sheu et al., 2013; Diabat et al., 2015; Hadj Youssef et al., 2018; Zhang et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Implementing a bivariate ordering and replacement policy for deteriorating systems with two failure types

Dong

Yang

et al. 2022

Int Trans Operational Res

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For plenty of existing maintenance policies in literature, it is always assumed that the spare unit for replacement is available at any time if needed, while this perquisite may be unrealistic or even impossible. In this paper, we investigate an ordering and replacement modeling framework for a deteriorating repairable system with two failure types, involving a repairable failure (Type I failure) and a nonrepairable failure (Type II failure). A regular order and an expedited order are both incorporated into the spare unit ordering policy while a preventive replacement and a corrective replacement are both considered into the replacement policy, of which the ordering and replacement policy is scheduled in regard to the number of repairable failures. The expected profit per unit of time over an infinite time span is maximized to seek the optimal solutions in terms of the renewal reward theorem with a recursive algorithm procedure. An illustrative example is designed to validate the applicability and effectiveness of the developed ordering and replacement policy. In addition, the sensitivity of the optimal ordering‐replacement policy is studied and the effect of some key characteristics on the optimal decisions is discussed.

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“…Then, the system is replaced by an ordered spare at failure or at preset age whichever occurs first and the optimization models regard as a decision variable. Armstrong and Atkins [17,18] extended the work of Osaki and Yamada [11] to optimize the age replacement interval and the ordering time simultaneously, and the study reported by Park and Sun [19] considered these two decision variables as well based on the work of Sheu et al [15]. To the best of our knowledge, the study of Wang et al [20] proposed firstly a joint policy of inspection-based PM and spare ordering; moreover, the optimization model is established and used for the determination of the optimal thresholds related to spare ordering, preventive replacement, and failure.…”

Section: Introductionmentioning

confidence: 99%

“…Some works have investigated and presented the joint optimization model of preventive maintenance policy and spare ordering policy for single-unit systems. Most of the past studies modeled the age-based replacement policy and spare provisioning policy jointly; we can refer to Osaki and Yamada [11], Sheu and Griffith [12], Jhang [13], Sheu and Chien [14], and Sheu et al [15], Sheu et al [16]. In the proposed models of these works, only one spare is ordered at time 0, that is, the initial time of the system operation, and is delivered after a fixed or random lead time.…”

Section: Introductionmentioning

confidence: 99%

A Joint Inspection-Based Preventive Maintenance and Spare Ordering Optimization Policy Using a Three-Stage Failure Process

et al. 2017

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Separated decision-making for maintenance and spare ordering is unrealistic in the industry, so this paper aims to optimize them together. A joint policy of inspection-based preventive maintenance (PM) and spare ordering considering two modes of spare ordering, namely, a regular order and an emergency order, is proposed for single-unit systems using a three-stage failure process. If the system is recognized to be in the minor defective stage, the original inspection interval is shortened and a regular order is placed. However, replacement is undertaken preventively or correctively if the severe defective stage is identified or a failure occurs. Depending on the system state and the state of the regular ordered spare when replacement is needed, all possible scenarios are considered to construct optimization model I. The decision variables are the optimal inspection interval and the times of shortening the original inspection interval. Additionally, model II on the basis of an assumption that the spare is always ordered at time 0 is also developed. The results from a numerical example illustrated the applicability and the effectiveness of model compared to model II, and the irregular inspection policy is validated to be cost-saving compared to the regular inspection policy.

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Age replacement policy with lead-time for a system subject to non-homogeneous pure birth shocks

Cited by 13 publications

References 30 publications

An overview of some classical models and discussion of the signature‐based models of preventive maintenance

An overview of some classical models and discussion of the signature‐based models of preventive maintenance

Implementing a bivariate ordering and replacement policy for deteriorating systems with two failure types

A Joint Inspection-Based Preventive Maintenance and Spare Ordering Optimization Policy Using a Three-Stage Failure Process

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