Interval and BUI type basic uncertain information in multi-sources evaluation and rules based decision making

Jin, LeSheng; Yager, Ronald R.; Chen, Zhen‐Song; Špirková, Jana; Mesiar, Radko

doi:10.1080/03081079.2022.2142217

Cited by 10 publications

(1 citation statement)

References 29 publications

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“…Many extensions of the TOPSIS method are based on fuzzy numbers [49] or interval numbers [50]. However, new types of uncertainty data, such as basic uncertain information and others, are still being developed [51]. Such approaches are associated with uncertainty in the data, as in some problems, the attribute values of alternatives to criteria cannot be unambiguously determined.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sustainable Assessment of Electric Vehicles: A Comparative Study of the TOPSIS Technique with Interval Numbers for Uncertainty Management

Kaczyńska,

Sulikowski,

Wątróbski

et al. 2023

Energies

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The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed.

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Section: Introductionmentioning

confidence: 99%