A condition-based order-replacement policy for a single-unit system

Wang, Ling; Chu, Jian; Mao, Weijie

doi:10.1016/j.apm.2007.07.016

Cited by 72 publications

(25 citation statements)

References 29 publications

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“…For an overview of this stream, including a review of diagnostics and prognostics techniques, see Jardine et al (2006). Within this stream, also the reducing effect of condition based maintenance on spare parts supply costs has been studied; see Elwany and Gebraeel (2008), Wang et al (2008Wang et al ( , 2009, and Rausch and Liao (2010). As already stated, we distinguish ourselves from the latter studies by considering the effect of conditioning monitoring on spare parts supply without changing the maintenance policy.…”

Section: Related Literaturementioning

confidence: 99%

Condition based spare parts supply

Xiang

Basten

Kranenburg³

et al. 2017

Reliability Engineering & System Safety

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We consider a spare parts stock point that serves an installed base of machines.Each machine contains the same critical component, whose degradation behavior is described by a Markov process. We consider condition based spare parts supply, and show that an optimal, condition based inventory policy is 20% more efficient on average than a standard, state-independent base stock policy. We further propose an efficient and effective heuristic policy.

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Section: Related Literaturementioning

confidence: 99%

Condition based spare parts supply

Xiang

Basten

Kranenburg³

et al. 2017

Reliability Engineering & System Safety

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“…A gamma distribution is often used to characterize continuous wear processes with non-negative, stationary, and statistically independent increments starting from zero level [9] [17]. An advantage of the gamma distribution process is the existence of an explicit probability distribution function which permits feasible mathematical developments [18].…”

Section: Resultsmentioning

confidence: 99%

“…≥ at the time of inspection, an immediate corrective maintenance is performed on the failed unit [17]. If its deterioration state i x exceeds the critical level…”

Section: System Characteristicsmentioning

confidence: 99%

Optimal Maintenance Modeling for Systems with Multiple Non-Identical Units Using Extended DSSP Method

Zhang¹,

Zeng²

2016

AJOR

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In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy; a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which "hard failure" and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.

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“…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. However, it is assumed that inspections take place with a regular interval and only one kind of ordering mode is taken into account.…”

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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A condition-based order-replacement policy for a single-unit system

Cited by 72 publications

References 29 publications

Condition based spare parts supply

Condition based spare parts supply

Optimal Maintenance Modeling for Systems with Multiple Non-Identical Units Using Extended DSSP Method

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

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