2011
DOI: 10.1016/j.ress.2011.05.004
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Maintenance scheduling of a manufacturing system subject to deterioration

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Cited by 31 publications
(21 citation statements)
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“…[46] not only determined the inspection schedule based on the system state but also the preventive replacement threshold. Similar works on the joint determination of both optimal inspection strategy and optimal repair policy for a manufacturing system whose result is dependent on the system condition can be found in [47], [48] and [49].…”
Section: Condition Based Maintenancementioning
confidence: 78%
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“…[46] not only determined the inspection schedule based on the system state but also the preventive replacement threshold. Similar works on the joint determination of both optimal inspection strategy and optimal repair policy for a manufacturing system whose result is dependent on the system condition can be found in [47], [48] and [49].…”
Section: Condition Based Maintenancementioning
confidence: 78%
“…A typical cluster tool is a "mini" manufacturing system of interacting subsystems (multiple chambers as manufacturing stations, supported by a material handling system) and can be seen as a quintessential FMS, since each chamber of a cluster tool can be seen as a manufacturing station and different wafer layers produced in these chambers can be seen as different operations performed in the corresponding manufacturing stations, while different wafer types pushed through the tool can be seen as different product types produced in this system. (31,49), (24,42), (11,29), (11,29)] and returns to state 2 [ (4,22), (11,29), (8,26), (1,19), (1,19)] and returns to state 3…”
Section: Resultsmentioning
confidence: 99%
“…Roeloffs , Kander and Raviv , and also Beichelt separately looked at the case where T 's distribution is partially known (only one percentile of T being known). Like in most papers published (e.g., Barlow et al , Luss and Kander , Kabir and El Tamimi , and more recent papers such as Wang , Ahmadi and Newby , Wang , Caballé et al , and Sheu et al ), in this paper, we assume that T 's distribution is completely known. The assumptions of the model are as follows: When working, the system generates or brings in revenue at a constant rate of c R per unit of time. The time it takes to complete each and every inspection is negligible, and each inspection costs an amount of c I – this is a common assumption in most papers on inspection replacement models that have been published .…”
Section: A Simple Finite Planning Horizon Inspection Modelmentioning
confidence: 99%
“…He has derived results for determining when it is prudent to carry out at least one inspection over a finite planning horizon for the case of imperfect inspections. Usher et al who have discussed the case of a finite planning horizon with minimization of costs as their objective function. Nakagawa et al who have discussed the application of basic inspection policies over a finite time span to five models: back‐up for a hard disk, checkpoint for double modular redundancy, job partition, garbage collection, and network partition. Nakagawa and Mizutani who have developed three replacement policies for a one‐unit system; for the replacement policies, n identical units are sequentially replaced over a finite period [0, L ] in accordance with some set rules. Taghipour et al who have proposed a model to find the optimal periodic inspection interval on a finite time horizon for a complex repairable system. The system has components that can experience ‘hard failures’ (which are detected as and when they occur) and ‘soft failures’, which are only detected when an inspection is carried out. Ahmadi and Newby who use a new approach (which they defined as the intensity control model) at determining an optimal inspection schedule over a production run of finite length L with the sole objective of minimizing overall costs. Berrade et al who have researched on periodic inspections being conducted on a system over a finite planning horizon of length L . The inspections in their paper are imperfect, and the criterion they use is minimization of total cost over the planning horizon.…”
Section: Introductionmentioning
confidence: 99%
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