PMID- 36893100
OWN - NLM
STAT- MEDLINE
DCOM- 20230313
LR  - 20230420
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 18
IP  - 3
DP  - 2023
TI  - Additive manufacturing process selection for automotive industry using 
      Pythagorean fuzzy CRITIC EDAS.
PG  - e0282676
LID - 10.1371/journal.pone.0282676 [doi]
LID - e0282676
AB  - For many different types of businesses, additive manufacturing has great 
      potential for new product and process development in many different types of 
      businesses including automotive industry. On the other hand, there are a variety 
      of additive manufacturing alternatives available today, each with its own unique 
      characteristics, and selecting the most suitable one has become a necessity for 
      relevant bodies. The evaluation of additive manufacturing alternatives can be 
      viewed as an uncertain multi-criteria decision-making (MCDM) problem due to the 
      potential number of criteria and candidates as well as the inherent subjectivity 
      of various decision-experts engaging in the process. Pythagorean fuzzy sets are 
      an extension of intuitionistic fuzzy sets that are effective in handling 
      ambiguity and uncertainty in decision-making. This study offers an integrated 
      fuzzy MCDM approach based on Pythagorean fuzzy sets for assessing additive 
      manufacturing alternatives for the automotive industry. Objective significance 
      levels of criteria are determined using the Criteria Importance Through 
      Inter-criteria Correlation (CRITIC) technique, and additive manufacturing 
      alternatives are prioritized using the Evaluation based on Distance from Average 
      Solution (EDAS) method. A sensitivity analysis is performed to examine the 
      variations against varying criterion and decision-maker weights. Moreover, a 
      comparative analysis is conducted to validate the acquired findings.
CI  - Copyright: (c) 2023 Menekse et al. This is an open access article distributed under 
      the terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Menekse, Akin
AU  - Menekse A
AUID- ORCID: 0000-0001-7519-3749
AD  - Istanbul Technical University, Istanbul, Turkey.
FAU - Ertemel, Adnan Veysel
AU  - Ertemel AV
AUID- ORCID: 0000-0002-5028-1096
AD  - Faculty of Management, Istanbul Technical University, Istanbul, Turkey.
FAU - Camgoz Akdag, Hatice
AU  - Camgoz Akdag H
AD  - Faculty of Management, Istanbul Technical University, Istanbul, Turkey.
FAU - Gorener, Ali
AU  - Gorener A
AUID- ORCID: 0000-0001-6000-5143
AD  - Faculty of Management, Istanbul Commerce University, Istanbul, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20230309
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - *Decision Making
MH  - *Fuzzy Logic
MH  - Industry
MH  - Uncertainty
MH  - Commerce
PMC - PMC9997986
COIS- The authors have declared that no competing interests exist
EDAT- 2023/03/10 06:00
MHDA- 2023/03/14 06:00
PMCR- 2023/03/09
CRDT- 2023/03/09 13:33
PHST- 2023/01/05 00:00 [received]
PHST- 2023/02/19 00:00 [accepted]
PHST- 2023/03/09 13:33 [entrez]
PHST- 2023/03/10 06:00 [pubmed]
PHST- 2023/03/14 06:00 [medline]
PHST- 2023/03/09 00:00 [pmc-release]
AID - PONE-D-23-00349 [pii]
AID - 10.1371/journal.pone.0282676 [doi]
PST - epublish
SO  - PLoS One. 2023 Mar 9;18(3):e0282676. doi: 10.1371/journal.pone.0282676. 
      eCollection 2023.