An efficiency-based interactive dynamic technique with interval-valued hesitant fuzzy constraint cone for rescue route planning

Wan, Shouhong; Chen, Zehui; Dong, Jiuying

doi:10.1016/j.eswa.2023.120648

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Since the introduction of IVHFSs, they have been successfully used in many practical problems, especially in the field of MCDM problems [14][15][16]. Existing interval-valued hesitant fuzzy MCDM methods can generally be divided into three classes.…”

Section: Introductionmentioning

confidence: 99%

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Multi-criteria decision-making based on the combination of interval-valued hesitant fuzzy information and ORESTE method

Li,

Niu,

Chen

et al. 2024

Preprint

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As an important extension of hesitant fuzzy sets, interval-valued hesitant fuzzy sets (IVHFSs) show the flexibility of decision makers (DMs) in expressing hesitant information. Accordingly, numerous research findings have been provided since the introduction of IVHFSs. However, a few important issues in IVHFS utilization remain to be addressed. To do this, this study introduces a multi-criteria decision-making (MCDM) method based on the combination of interval-valued hesitant fuzzy information and the method of the French organization Rangement et Synthese de Ronnees Relationnelles (ORESTE). First, the shortcomings of generalized normalized Hamming distance for interval-valued hesitant fuzzy elements (IVHFEs) in previous studies are discussed. Subsequently, several novel distance measures and a possibility degree formula are developed. Meanwhile, the proofs of the properties are provided to illustrate the effectiveness of the proposed distance measures and possibility degree formula. Second, an MCDM method based on the combination of interval-valued hesitant fuzzy information and the ORESTE method is developed. Lastly, an MCDM problem of identifying the optimal bidding schemeis presented to demonstrate the effectiveness of the proposed method. Acomparative study with other methods is conducted with an identical illustrative example.

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Section: Introductionmentioning

confidence: 99%

“…The third class is composed of consistency and consensus measures. For example, Wan et al [15] developed the methods of repairing and judgement the additive consistency of the interval-valued hesitant fuzzy preference relations. Liang et al [18] introduced a consensus reaching process able to smooth disagreements between the experts.…”

Section: Introductionmentioning

confidence: 99%