Novel MCGDM analysis under m-polar fuzzy soft expert sets

Multi-criteria group decision-making (MCGDM) is an approach to tackle different decision-making situations, where the criteria that drive the decision are considered by different experts. Research regarding MCGDM is quite challenging due to both the structure of the problem and the existence of comp...

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Bibliographic Details
Published in:Neural computing & applications 2021-09, Vol.33 (18), p.12051-12071
Main Authors: Akram, Muhammad, Ali, Ghous, Butt, Muhammad Arif, Alcantud, José Carlos R.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Damsites
Data Mining and Knowledge Discovery
Decision analysis
Decision making
Fuzzy logic
Fuzzy sets
Image Processing and Computer Vision
Multiple criterion
Operators (mathematics)
Original Article
Probability and Statistics in Computer Science
Site selection
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Summary:Multi-criteria group decision-making (MCGDM) is an approach to tackle different decision-making situations, where the criteria that drive the decision are considered by different experts. Research regarding MCGDM is quite challenging due to both the structure of the problem and the existence of complex uncertain information. The goal of this article is to produce a new MCGDM model which covers criteria evaluation by different experts. A novel hybrid model called m -polar fuzzy soft expert sets is developed by the combination of m -polar fuzzy sets with soft expert sets; thus, it investigates soft expert sets in the m -polar fuzzy environment. The characteristics of this hybrid model are explored with the aid of numerical examples. Further, its basic properties are investigated, and the operations of subsethood, complement, intersection, union, plus the OR and AND operators are introduced. Two well-known real-world problems are solved with the developed hybrid model, namely site selection for a dam and human trafficking analysis for different countries. The algorithm of the proposed model proves its efficiency and cogency. A comparison of the developed model with existing mathematical methods is provided.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05850-w