Multicriteria model for maintenance benchmarking
• This article describes the first multicriteria model to apply competitive and generic maintenance benchmarking. • Discrete probability distributions are used so this provides a significant improvement over the use of industrial averages. • The multicriteria model uses fuzzy AHP to include the ambi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Carnero, María Carmen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Umfang: |
19 |
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| Übergeordnetes Werk: |
Enthalten in: Lebenshilfe Berlin - Berlin : Landesverband, 1979, 33(2014), 2, Seite 303-321 |
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| Übergeordnetes Werk: |
volume:33 ; year:2014 ; number:2 ; pages:303-321 ; extent:19 |
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| DOI / URN: |
10.1016/j.jmsy.2013.12.006 |
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| 520 | |a • This article describes the first multicriteria model to apply competitive and generic maintenance benchmarking. • Discrete probability distributions are used so this provides a significant improvement over the use of industrial averages. • The multicriteria model uses fuzzy AHP to include the ambiguities and uncertainties involved in the judgment. • Discrete probability distributions were created in different industrial sectors. • The final utility of an alternative is calculated by a multi-measure utility function. | ||
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• This article describes the first multicriteria model to apply competitive and generic maintenance benchmarking. • Discrete probability distributions are used so this provides a significant improvement over the use of industrial averages. • The multicriteria model uses fuzzy AHP to include the ambiguities and uncertainties involved in the judgment. • Discrete probability distributions were created in different industrial sectors. • The final utility of an alternative is calculated by a multi-measure utility function. |
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• This article describes the first multicriteria model to apply competitive and generic maintenance benchmarking. • Discrete probability distributions are used so this provides a significant improvement over the use of industrial averages. • The multicriteria model uses fuzzy AHP to include the ambiguities and uncertainties involved in the judgment. • Discrete probability distributions were created in different industrial sectors. • The final utility of an alternative is calculated by a multi-measure utility function. |
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code="w">(DE-576)9165728920</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:33</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:2</subfield><subfield code="g">pages:303-321</subfield><subfield code="g">extent:19</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jmsy.2013.12.006</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">33</subfield><subfield code="j">2014</subfield><subfield code="e">2</subfield><subfield code="h">303-321</subfield><subfield code="g">19</subfield></datafield></record></collection>
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