PMID- 35571604
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221101
IS  - 2198-6053 (Electronic)
IS  - 2199-4536 (Print)
IS  - 2199-4536 (Linking)
VI  - 8
IP  - 6
DP  - 2022
TI  - Fermatean fuzzy copula aggregation operators and similarity measures-based 
      complex proportional assessment approach for renewable energy source selection.
PG  - 5223-5248
LID - 10.1007/s40747-022-00743-4 [doi]
AB  - Selecting the optimal renewable energy source (RES) is a complex multi-criteria 
      decision-making (MCDM) problem due to the association of diverse conflicting 
      criteria with uncertain information. The utilization of Fermatean fuzzy numbers 
      is successfully treated with the qualitative data and uncertain information that 
      often occur in realistic MCDM problems. In this paper, an extended complex 
      proportional assessment (COPRAS) approach is developed to treat the 
      decision-making problems in a Fermatean fuzzy set (FFS) context. First, to 
      aggregate the Fermatean fuzzy information, a new Fermatean fuzzy Archimedean 
      copula-based Maclaurin symmetric mean operator is introduced with its desirable 
      characteristics. This proposed operator not only considers the interrelationships 
      between multiple numbers of criteria, but also associates more than one marginal 
      distribution, thus avoiding information loss in the process of aggregation. 
      Second, new similarity measures are developed to quantify the degree of 
      similarity between Fermatean fuzzy perspectives more effectively and are further 
      utilized to compute the weights of the criteria. Third, an integrated Fermatean 
      fuzzy-COPRAS approach using the Archimedean copula-based Maclaurin symmetric mean 
      operator and similarity measure has been developed to assess and rank the 
      alternatives under the FFS perspective. Furthermore, a case study of RES 
      selection is presented to validate the feasibility and practicality of the 
      developed model. Comparative and sensitivity analyses are used to check the 
      reliability and strength of the proposed method.
CI  - (c) The Author(s) 2022.
FAU - Mishra, Arunodaya Raj
AU  - Mishra AR
AUID- ORCID: 0000-0001-9949-5813
AD  - Department of Mathematics, Government College Raigaon, Satna, MP India.
FAU - Rani, Pratibha
AU  - Rani P
AD  - Department of Mathematics, Rajiv Gandhi National Institute of Youth Development, 
      Sriperumbudur, TN India.
FAU - Saha, Abhijit
AU  - Saha A
AD  - Department of Mathematics, Techno College of Engineering Agartala, Maheshkhola, 
      Tripura 799004 India.
FAU - Senapati, Tapan
AU  - Senapati T
AD  - Department of Mathematics, Padima Janakalyan Banipith, Kukrakhupi, Jhargram, 
      721517 India.
FAU - Hezam, Ibrahim M
AU  - Hezam IM
AD  - Department of Statistics and Operations Research, College of Sciences, King Saud 
      University, Riyadh, Saudi Arabia. GRID: grid.56302.32. ISNI: 0000 0004 1773 5396
FAU - Yager, Ronald R
AU  - Yager RR
AD  - Machine Intelligence Institute, Iona College, New Rochelle, NY 10801 USA. GRID: 
      grid.419406.e. ISNI: 0000 0001 0087 8225
LA  - eng
PT  - Journal Article
DEP - 20220510
PL  - Germany
TA  - Complex Intell Systems
JT  - Complex & intelligent systems
JID - 9918284259706676
PMC - PMC9086431
OTO - NOTNLM
OT  - Archimedean copula
OT  - COPRAS
OT  - Fermatean fuzzy set
OT  - Maclaurin mean operator
OT  - Renewable energy
OT  - Similarity measure
COIS- Conflict of interestAll authors declare that they have no conflict of interest.
EDAT- 2022/05/17 06:00
MHDA- 2022/05/17 06:01
PMCR- 2022/05/10
CRDT- 2022/05/16 04:02
PHST- 2021/09/05 00:00 [received]
PHST- 2022/03/30 00:00 [accepted]
PHST- 2022/05/17 06:00 [pubmed]
PHST- 2022/05/17 06:01 [medline]
PHST- 2022/05/16 04:02 [entrez]
PHST- 2022/05/10 00:00 [pmc-release]
AID - 743 [pii]
AID - 10.1007/s40747-022-00743-4 [doi]
PST - ppublish
SO  - Complex Intell Systems. 2022;8(6):5223-5248. doi: 10.1007/s40747-022-00743-4. 
      Epub 2022 May 10.