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Linguistic Z-numbers and cloud model weighted ranking technology and its application in concept evaluation of information axiom

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Abstract

Concept design evaluation is the most critical part of product design, which determines the direction of the subsequent design stage. Due to the various uncertainties inherent in decision-maker (DM)’s linguistic evaluation, such as fuzziness and randomness, how to deal with these uncertainties reasonably is a challenge. Only using a single uncertainty treatment method may not be enough to select an appropriate conceptual design. Therefore, this paper aims to develop an extended information axiom concept design evaluation model combining linguistic Z-numbers (LZNs) and axiomatic design methods to deal with the uncertainty in concept design evaluation. Specifically, the LZNs are used to express DM's subjective evaluation information and the reliability of the evaluation result. In the evaluation process, the uncertainty caused by linguistic variables is represented by the cloud model. Moreover, the compatibility weights of concepts are derived objectively based on the compatibility matrix. Finally, the design combination with the minimum information content is obtained by using the extended information axiom. The feasibility and applicability of this method are verified by an evaluation example of ink pen concept design. Compared with the existing methods, the proposed method considers more uncertainty and is more in line with the actual situation.

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Acknowledgements

This research was funded by the Youth Science and Technology Talent Growth Project by the Department of Education of Guizhou Province (Grant numbers: KY [2017]106 and KY [2018]112), the Science and Technology Foundation of Guizhou Province (Grant number: [2020]1Y262 and [2020]1Y232) and Guizhou University Talent Fund (Grant numbers: 2019-07).

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, China

    Qinghua Liu, Jiadui Chen, Yongming Wu & Kai Yang

  2. State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China

    Yongming Wu

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  1. Qinghua Liu
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  2. Jiadui Chen
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Qinghua Liu was involved in conceptualization, methodology, validation, formal analysis, writing—original draft, writing—review and editing. Jiadui Chen contributed to conceptualization, writing—review and editing, project administration, supervision, funding acquisition. Yongming Wu was involved in investigation, formal analysis, writing—review and editing. Kai Yang contributed to writing—review and editing, funding acquisition, formal analysis.

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Correspondence to Jiadui Chen.

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Liu, Q., Chen, J., Wu, Y. et al. Linguistic Z-numbers and cloud model weighted ranking technology and its application in concept evaluation of information axiom. J Supercomput 78, 6061–6089 (2022). https://doi.org/10.1007/s11227-021-04106-7

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