Selective Maintenance Decision-Making Over Extended Planning Horizons

Maillart, Lisa M.; Cassady, C. Richard; Rainwater, Chase; Schneider, Kellie

doi:10.1109/tr.2009.2026689

Cited by 74 publications

(34 citation statements)

References 14 publications

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“…5 to find the optimal reliability of the system at state b = 3. The vector of component's state at the time of exiting the maintenance depot is X ¼ 4 0 4 3 3 1 3 ½ , that is, we select to repair components 1 and 3 in subsystem 1 to state 4, components (2, 1), (2,2) and (2,4) in subsystem 2 to state 3, do nothing to components (1,2) and (2,3). We also solve the SM model for the case that there is no benefit of repairing multiple components.…”

Section: Solution Approach and Examplementioning

confidence: 99%

“…When there is no benefit of repairing multiple components, we restore 4 components (1, 1), (1,3), (2,1), (2,4) to an intermediate state 3 and do nothing to other 3 components (2, 2), (1, 2), (2, 3), while components (1, 1), (1,3) can be maintained to the highest state (perfect functioning state) and another maintenance action can be performed on component (2, 2) (from state 2 to state 3) if the benefits of repairing multiple components are considered. Consequently, a significantly higher system reliability at level 3 can be achieved, R s (3) = 0.945, and the resources utilization is also higher by 18.8 cost units and 14.6 time units when considering the advantages of repairing multiple components simultaneously.…”

Section: Solution Approach and Examplementioning

confidence: 99%

“…Schneider and Cassady [3] extended the model in [1] by considering selective maintenance for a fleet including multiple binary series-parallel systems. Maillart et al [4] considered selective maintenance optimization for binary series-parallel systems working under multiple identical missions. Pandey et al [5] studied the selective maintenance for a binary system under an age-based imperfect maintenance, in which the condition of components after maintenance depends on the cost spent and the maintenance may bring a component to a state which may be not "as good as new".…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selective Maintenance for Multi-state Systems Considering the Benefits of Repairing Multiple Components Simultaneously

Dao

Zuo

2014

Lecture Notes in Mechanical Engineering

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Many industrial systems such as aircrafts, ships, manufacturing systems, etc. are required to perform several missions with finite breaks between missions. Maintenance is only available within the breaks. Due to the limitation of resources, all components in the system may not be maintained as desired. The selective maintenance problem helps the decision makers figure out what critical components to select and how to perform maintenance on these components. This paper studies the selective maintenance for multi-state series-parallel systems with the benefit of repairing multiple components simultaneously. Both time and cost savings can be acquired when several components are simultaneously repaired in a selective maintenance strategy. As the number of repaired components increases, the saved time and cost will also increase due to the share of setting up between components and another additional reduction amount from the repair of multiple identical components. A non-linear optimization model is developed to find the most reliable system subjected to time and cost constraints. Genetic algorithm is used to solve the optimization model. An illustrative example will be provided.

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Section: Solution Approach and Examplementioning

confidence: 99%

Section: Solution Approach and Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selective Maintenance for Multi-state Systems Considering the Benefits of Repairing Multiple Components Simultaneously

Dao

Zuo

2014

Lecture Notes in Mechanical Engineering

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“…However, since the system is required to perform a sequence of missions, while requiring short development schedules and very high reliability, it becomes increasingly important to develop appropriate approaches to manage selective maintenance decisions when the planning horizon considers more than a single mission. In (Maillart, Cassady, Rainwater, and Schneider 2005), the authors consider a series-parallel system where each subsystem is composed of identical components whose time to failure is exponentially distributed. The system is assumed to operate a sequence of identical missions such that breaks between two successive missions are of equal durations.…”

Section: Introductionmentioning

confidence: 99%

“…Numerical experiments are conducted to perform and compare the results obtained for three maintenance optimization problems. Nevertheless, the approach proposed in (Maillart, Cassady, Rainwater, and Schneider 2005), merely relies on a series-parallel system with few subsystems each composed of iid components. Furthermore, replacement of failed components is the only available maintenance action, missions are of identical time interval and breaks are also of identical durations.…”

Section: Introductionmentioning

confidence: 99%

The CoVeR method for the maintenance of a rail corridor

Khatab¹,

Dahane²,

Aït‐Kadi³

2013

Safety, Reliability and Risk Analysis

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This paper addresses the selective maintenance optimization problem for a multi-mission series-parallel system. Such a system experiences several missions with breaks between successive missions. The reliability of the system is given by the conditional probability that the system survives the next mission given that it has survived the previous mission. The reliability of each system component is characterized by its hazard function. To maintain the reliability of the system, preventive maintenance actions are performed during breaks. Each preventive maintenance action is characterized by its age reduction coefficient. The selective maintenance problem consists in finding an optimal sequence of maintenance actions, to be performed within breaks, so that to minimize the total maintenance cost while providing a given required system reliability level for each mission. To solve such a combinatorial optimization problem, an optimization method is proposed on the basis of the genetic algorithm. A numerical example is provided for illustration.

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A literature review on selective maintenance for multi‐unit systems

Cao

Jia

et al. 2018

Quality & Reliability Eng

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Selective maintenance is regarded as a type of profit-generating maintenance policy, playing an important role in balancing limited maintenance resources with system performance. Since 1988, increasing interest has been focused on this research area. Nevertheless, to the best of our knowledge, there is a lack of critical reviews of selective maintenance. This paper is the first systematic review focusing on this relevant topic. In this work, a definition and some specific features of selective maintenance are elaborated. Based on these features, a set of criteria that have been considered in selective maintenance optimization are summarized into 3 categories: system characteristics, maintenance characteristics, and mission profile characteristics. Based on these criteria, a comprehensive literature review on selective maintenance is undertaken. The solution approaches, as well as a general procedure for selective maintenance optimization, are discussed. Finally, some possible directions for further research are provided.

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Selective Maintenance Decision-Making Over Extended Planning Horizons

Cited by 74 publications

References 14 publications

Selective Maintenance for Multi-state Systems Considering the Benefits of Repairing Multiple Components Simultaneously

Selective Maintenance for Multi-state Systems Considering the Benefits of Repairing Multiple Components Simultaneously

The CoVeR method for the maintenance of a rail corridor

A literature review on selective maintenance for multi‐unit systems

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