Neutrosophic Sets and Systems (Jan 2023)

Some Aggregation Operators of Credibility Interval Trapezoidal Fuzzy Neutrosophic Numbers and Their Decision-Making Application of Landslide Control Design Schemes

  • Wanlu Du,
  • Shigui Du,
  • Jun Ye

DOI
https://doi.org/10.5281/zenodo.7535955
Journal volume & issue
Vol. 53
pp. 49 – 74

Abstract

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As a generalization of trapezoidal fuzzy neutrosophic numbers (TFNNs), credibility trapezoidal fuzzy neutrosophic numbers (C-TFNNs) can independently describe true, false, and indeterminate membership degrees and their credibility levels in uncertain and inconsistent scenarios. Since the true, false, and indeterminate membership degrees are closely related to their credibility levels, C-TFNN can ensure the credibility of TFNN, which shows its clear merit. However, C-TFNNs cannot expresses the interval membership degrees of the truth, falsity and indeterminacy and the uncertain credibility levels, which are produced due to human cognitive vagueness, incompleteness, and uncertainty. Furthermore, existing decision models of C-TFNNs cannot perform such a DM issue with both ITFNNs and uncertain credibility levels, which reveals a gap. To compensates for this gap. this paper extends C-TFNNs to credibility interval TFNNs (C ITFNNs), which strengthens the expression capability of uncertain information. Then, the operational laws and score function of C-ITFNNs are defined to solve the aggregation and sorting issues of C-ITFNNs in decision-making (DM) problems. Subsequently, the C-ITFNN weighted geometric averaging (C-ITFNNWGA) and C-ITFNN weighted arithmetic averaging (C ITFNNWAA) operators are proposed in view of operational laws of C-ITFNNs. Furthermore, a multi-attribute DM model is established in terms of the two aggregation operators and the score function in the C-ITFNN circumstance. Finally, a DM case of landslide control design schemes is used to reveal the applicability of the proposed DM model in the C-ITFNN scenario. By comparative analysis, the main superiority of our new DM model is that it not only compensates for the gap of existing DM models, but also is more reliable and versatile than existing DM models.

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