Journal
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Volume 37, Issue 1, Pages 596-624Publisher
WILEY
DOI: 10.1002/int.22641
Keywords
BWM; cloud model; hesitant fuzzy linguistic hybrid cloud; multicriteria decision making
Categories
Funding
- National Major Science and Technology Projects of China [2017-I-00070008, 2017-I-0011-0012]
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This paper introduces a novel hesitant fuzzy linguistic hybrid cloud model to effectively handle the complex uncertainties in multicriteria decision making. By integrating hesitant fuzzy linguistic term sets and cloud models, this model can avoid information loss and distortion, providing more consistency and reliability compared to traditional methods. The proposed method also includes an improved approach for aggregating multiple linguistic concepts.
Developing effective and accurate model to handle complex uncertainties of linguistic assessments in multicriteria decision making (MCDM) has important theoretical significance and practical value of engineering. This paper proposes a novel hesitant fuzzy linguistic hybrid cloud (HFLHC) model that integrates hesitant fuzzy linguistic term set and cloud model to handle the hesitancy, fuzziness, and randomness of linguistic expression. The normal cloud and trapezium cloud are integrated to represent hybrid-length linguistic variables of HFLHC model, which can effectively avoid evaluation information loss and distortion. Aiming at applying HFLHC model to MCDM, some hybrid operations for normal cloud and trapezium cloud are developed. Moreover, an improved method for aggregating multiple linguistic concepts into an integrated trapezium cloud in HFLHC model is proposed, with consideration of the different representation region of each linguistic concept. Furthermore, a novel HFLHC-based best-worst method is proposed to obtain optimal criteria weights with developing a HFLHC optimization programming model and a modified consistency ratio. Finally, an illustrative example of sustainable supplier selection is presented. Several comparative analyses demonstrate that our method can provide more consistency and greater reliability.
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