Trustworthy collaborative evaluation of multi-service subjects in the cloud manufacturing model
In the context of cloud manufacturing, challenges related to trust, including malicious deception and dishonest feedback, are exacerbated by information asymmetry among platform participants. To address these issues, a novel approach for evaluating collaboration credibility among multi-service subje...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014467 |
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| Summary: | In the context of cloud manufacturing, challenges related to trust, including malicious deception and dishonest feedback, are exacerbated by information asymmetry among platform participants. To address these issues, a novel approach for evaluating collaboration credibility among multi-service subjects within the cloud manufacturing framework is introduced. Initially, an evaluation index system is constructed, incorporating both internal and external data from the platform. This system is framed around four critical dimensions: subject characteristics, service characteristics, product characteristics, and task characteristics. The attribute weights are determined using an integrated assignment method. Subsequently, to effectively address the issues of ambiguity, uncertainty and randomness of evaluation information in the integrated evaluation process, this paper proposed a comprehensive evaluation model. This model capitalizes on the strengths of intuitionistic fuzzy sets (IFSs) and cloud models in converting qualitative assessments into quantitative evaluations, and leverages the method of approximation of the order of ideal solutions (TOPSIS) to carry out a comprehensive assessment of the degree of trustworthy collaboration of the service subject. The practicality and validity of the proposed methodology are demonstrated through a case study analysis, which confirms the model's effectiveness in enhancing the reliability of collaborative evaluations under the cloud manufacturing model. |
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| ISSN: | 1110-0168 |