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Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics
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Sentiment Analysis using TF-IDF Weighting of UK MPs’ Tweets on Brexit
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Proportional hesitant 2-tuple linguistic distance measurements and extended VIKOR method: Case study of evaluation and selection of green airport plans
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A Fuzzy Approach to Sentiment Analysis at the Sentence Level
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Fuzzy convolutional deep-learning model to estimate the operational risk capital using multi-source risk events
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Type-1 OWA Operators in Aggregating Multiple Sources of Uncertain Information: Properties and Real-World Applications in Integrated Diagnosis.
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Genetic Algorithm-Based Fuzzy Inference System for Describing Execution Tracing Quality
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Consistency improvement with a feedback recommendation in personalized linguistic group decision making
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Attitude Quantifier Based Possibility Distribution Generation Method for Hesitant Fuzzy Linguistic Group Decision Making
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Multi-stage consistency optimization algorithm to decision-making with incomplete probabilistic linguistic preference relation
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Revisiting Fuzzy and Linguistic Decision-Making: Scenarios and Challenges for Wiser Decisions in a Better Way
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ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages
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Personalized individual semantics-based approach for large scale failure mode and effect analysis with incomplete preference information
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Abstract:
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. ; Failure modes and effects analysis (FMEA) is a very useful reliability-management instrument for detecting and mitigating risks in various fields. Linguistic assessment approach has recently been widely used in FMEA. Words mean different things to different people, so FMEA members may present personalized individual semantics (PIS) in their linguistic assessment information. This paper designs a PIS-based FMEA approach with members expressing their opinions over failure modes and risk factors using linguistic distribution assessment matrices (LDAMs) and also provide their opinions over failure modes using incomplete additive preference relations (APRs). A preference information preprocessing method with a two-stage optimization model is presented to generate complete APRs with acceptable consistency levels from incomplete APRs. Then, a deviation minimum-based optimization model is designed to personalize individual semantics by minimizing the deviation between APR and the numerical assessment matrix derived from the corresponding LDAM. This is followed by the developing of a ranking process to generate the risk ordering of failure modes. A case study and a detailed comparison analysis are presented to show the effectiveness of the PIS-based linguistic FMEA approach.
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URL: https://doi.org/10.1080/24725854.2020.1731774 https://dora.dmu.ac.uk/handle/2086/19272
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Consensus and opinion evolution-based failure mode and effect analysis approach for reliability management in social network and uncertainty contexts
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Type-1 OWA operators in aggregating multiple sources of uncertain information : properties and real world applications
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Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets
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In: ISSN: 0884-8173 ; EISSN: 1098-111X ; International Journal of Intelligent Systems ; https://www.hal.inserm.fr/inserm-03026626 ; International Journal of Intelligent Systems, Wiley, 2019, 34 (6), pp.1261-1280. ⟨10.1002/int.22095⟩ (2019)
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Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects
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