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https://doi.org/10.1109/robot....
Article . 2002 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2017
Data sources: DBLP
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Modeling re-entrant manufacturing systems with inspections

Authors: Narahari, Y; Khan, LM;

Modeling re-entrant manufacturing systems with inspections

Abstract

Inspection stations are now an integral part of any manufacturing system and help track product quality and process performance. In this paper the authors consider re-entrant manufacturing systems (such as semiconductor fabrication facilities) with inspections at various stages of processing. At the end of each inspection, three possibilities are assumed, namely accept, reject, or rework at some previous stage. The authors propose re-entrant lines with probabilistic routing as models for such systems and present an efficient analytical technique based on mean value analysis to predict mean cycle times and throughput rates. The method can be used to compare different ways of locating inspection stations, from a cycle time and throughput viewpoint.

Country
India
Related Organizations
Keywords

Computer Science & Automation (Formerly, School of Automation)

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green