Assessment and prioritization method of key engineering characteristics for complex products based on cloud rough numbers
Accurate assessment of engineering characteristics in the design phase is vital for complex products because it can effectively determine the orientation of subsequent design practice strategies. This assessment can be regarded as a multi-attribute group decision-making procedure comprising two part...
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| Vydané v: | Advanced engineering informatics Ročník 49; s. 101309 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.08.2021
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| Predmet: | |
| ISSN: | 1474-0346, 1873-5320 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Accurate assessment of engineering characteristics in the design phase is vital for complex products because it can effectively determine the orientation of subsequent design practice strategies. This assessment can be regarded as a multi-attribute group decision-making procedure comprising two parts: construction of engineering characteristic indicator system and identification of key engineering characteristics (KECs). Previous studies have provided many indicator systems to extract the KECs that affect the whole system significantly. However, they usually only consider a few factors and ignore the influence of low-dimension factors. In addition, the KEC identification usually is conducted based on the assessment of decision-makers, which involves intrapersonal judgment fuzziness and interpersonal consensus inconsistency. However, existing studies still have some limitations, such as only considering single intrapersonal or interpersonal vagueness, lacking the mechanism to manipulate various uncertainties simultaneously, and inadequate calculating the relative weights of experts and evaluation criteria, which may weaken the accuracy of final results. Therefore, this study develops an integrated assessment and prioritization method of engineering characteristics by constructing a comprehensive list of engineering characteristic indicators, developing the cloud rough number model that can simultaneously handle various uncertainties, and considering the objectively optimal weight of both experts and criteria. Additionally, a well-defined Excel computational program is also presented to reduce the calculation. Finally, a real case of KECs prioritization and a result comparison to the previous methods are presented to demonstrate its feasibility and effectiveness. |
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| ISSN: | 1474-0346 1873-5320 |
| DOI: | 10.1016/j.aei.2021.101309 |