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A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria
by
Botelho Tiago
, Tavana Madjid
, Chalvatzis Konstantinos
, Amin, Vafadarnikjoo
in
Decision making
/ Mathematical analysis
/ Multiple criterion
/ Operations research
/ Vectors (mathematics)
2020
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A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria
by
Botelho Tiago
, Tavana Madjid
, Chalvatzis Konstantinos
, Amin, Vafadarnikjoo
in
Decision making
/ Mathematical analysis
/ Multiple criterion
/ Operations research
/ Vectors (mathematics)
2020
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Do you wish to request the book?
A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria
by
Botelho Tiago
, Tavana Madjid
, Chalvatzis Konstantinos
, Amin, Vafadarnikjoo
in
Decision making
/ Mathematical analysis
/ Multiple criterion
/ Operations research
/ Vectors (mathematics)
2020
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A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria
Journal Article
A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria
2020
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Overview
The best–worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating ≤a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers’ (DMs’) confidence in their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs’ confidence in the best-to-others preferences and the DMs’ confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs.
Publisher
Springer Nature B.V
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