Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014

By Abbas Mardani, Ahmad Jusoh, Edmundas Kazimieras Zavadskas. Expert systems with applications, 2015

930 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management, Monitoring, Policy and Law, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/69d193a7fda833a3ac22839900b90ae93367310e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Seyed Hossein Razavi Hajiagha
2. Shide Sadat Hashemi
3. Jurgita Antucheviciene
4. Jaroslaw Watrobski
5. Valerie Belton
6. Judit Lienert
7. Mika Marttunen
8. E. Lozano-Mínguez

In Management, Monitoring, Policy and Law

1. Valerie Belton
2. Judit Lienert
3. Mika Marttunen
4. E. Lozano-Mínguez
5. Edmundas Kazimieras Zavadskas
6. Jurgita Antucheviciene
7. Jaroslaw Watrobski
8. Athanasios Kolios

In Management Science and Operations Research

1. Edmundas Kazimieras Zavadskas
2. Jaroslaw Watrobski
3. Jurgita Antucheviciene
4. Seyed Hossein Razavi Hajiagha
5. Shide Sadat Hashemi
6. E. Lozano-Mínguez
7. Athanasios Kolios
8. Konstantinos Salonitis

Citations in the corpus

• A systematic review and meta-Analysis of SWARA and WASPAS methods: Theory and applications with recent fuzzy developments (2017, neutral): “[75, 76] grouped MCDM and FMCDM tools in a different way.”
• The Interval-Valued Intuitionistic Fuzzy MULTIMOORA Method for Group Decision Making in Engineering (2015, methodology): “To deal with this challenge, several frameworks have been introduced, starting from application of Zadeh fuzzy numbers [6] and followed by numerous extensions of decision making methods using type 1 and type 2 fuzzy sets, involving interval-valued as well as intuitionistic fuzzy sets [69].”
• Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations (2017, methodology): “Concurrently, the diversity of applied MCDA methods has also increased in part because of a growing trend to combine different MCDA methods and also to integrate MCDA with other methods, particularly for handling uncertainty (Mardani et al., 2015).”
• A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs (2016, methodology): “Many instances of the applications of fuzzy MCDM methods can be found in [47,48], where it was highlighted that most of the applications had selected to implement variants based on AHP.”
• Generalised framework for multi-criteria method selection (2018, methodology): “New MCDA methods often use the fuzzy set theory [14] , which allows using uncertain data as trapezoidal fuzzy numbers, i.e. Ñ = (n l , n u , α F , β F ) , or triangular fuzzy numbers, i.e. Ñ = (n, α F , β F ) [53-55] .”