PMID- 35050472
OWN - NLM
STAT- MEDLINE
DCOM- 20220517
LR  - 20240329
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Print)
IS  - 0944-1344 (Linking)
VI  - 29
IP  - 25
DP  - 2022 May
TI  - An extended hybrid fuzzy multi-criteria decision model for sustainable and 
      resilient supplier selection.
PG  - 37291-37314
LID - 10.1007/s11356-021-17851-2 [doi]
AB  - The formalization and solution of supplier selection problems (SSPs) based on 
      sustainable (economic, environmental, and social) indicators have become a 
      fundamental tool to perform a strategic analysis of the whole supply chain 
      process and maximize the competitive advantage of firms. Over the last decade, 
      sustainability issues have been often considered in combination with resilient 
      indexes leading to the study of sustainable-resilient supplier selection problems 
      (SRSSPs). The current research on sustainable development, particularly concerned 
      with the strong impact that the recent COVID-19 pandemic has had on supply 
      chains, has been paying increasing attention to the resilience concept and its 
      role within SSPs. This study proposes a hybrid fuzzy multi-criteria decision 
      making (MCDM) method to solve SRSSPs. The fuzzy best-worst method is used first 
      to determine the importance weights of the selection criteria. A combined grey 
      relational analysis and the technique for order of preference by similarity to 
      ideal solution (TOPSIS) method is used next to evaluate the suppliers in a fuzzy 
      environment. Triangular fuzzy numbers (TFNs) are used to express the weights of 
      criteria and alternatives to account for the ambiguity and uncertainty inherent 
      to subjective evaluations. However, the proposed method can be easily extended to 
      other fuzzy settings depending on the uncertainty facing managers and 
      decision-makers. A real-life application is presented to demonstrate the 
      applicability and efficacy of the proposed model. Sixteen evaluation criteria are 
      identified and classified as economic, environmental, social, or resilient. The 
      results obtained through the case study show that "pollution control," 
      "environmental management system," and "risk awareness" are the most influential 
      criteria when studying SRSSPs related to the manufacturing industry. Finally, 
      three different sensitivity analysis methods are applied to validate the 
      robustness of the proposed framework, namely, changing the weights of the 
      criteria, comparing the results with those of other common fuzzy MCDM methods, 
      and changing the components of the principal decision matrix.
CI  - (c) 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Afrasiabi, Ahmadreza
AU  - Afrasiabi A
AD  - Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran.
FAU - Tavana, Madjid
AU  - Tavana M
AUID- ORCID: 0000-0003-2017-1723
AD  - Business Systems and Analytics Department, La Salle University, Philadelphia, PA, 
      USA. tavana@lasalle.edu.
AD  - Business Information Systems Department, Faculty of Business Administration and 
      Economics, University of Paderborn, Paderborn, Germany. tavana@lasalle.edu.
FAU - Di Caprio, Debora
AU  - Di Caprio D
AUID- ORCID: 0000-0002-6900-3977
AD  - Department of Economics and Management, University of Trento, Trento, Italy.
LA  - eng
PT  - Journal Article
DEP - 20220120
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
SB  - IM
MH  - *COVID-19
MH  - Decision Making
MH  - *Fuzzy Logic
MH  - Humans
MH  - Pandemics
MH  - Sustainable Development
MH  - Uncertainty
PMC - PMC8771628
OTO - NOTNLM
OT  - Best-worst method
OT  - Fuzzy logic
OT  - Grey relational analysis
OT  - Resilience
OT  - Supplier selection
OT  - Sustainability
OT  - TOPSIS
COIS- The authors declare no competing interests.
EDAT- 2022/01/21 06:00
MHDA- 2022/05/18 06:00
PMCR- 2022/01/20
CRDT- 2022/01/20 12:38
PHST- 2021/08/12 00:00 [received]
PHST- 2021/11/25 00:00 [accepted]
PHST- 2022/01/21 06:00 [pubmed]
PHST- 2022/05/18 06:00 [medline]
PHST- 2022/01/20 12:38 [entrez]
PHST- 2022/01/20 00:00 [pmc-release]
AID - 10.1007/s11356-021-17851-2 [pii]
AID - 17851 [pii]
AID - 10.1007/s11356-021-17851-2 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2022 May;29(25):37291-37314. doi: 
      10.1007/s11356-021-17851-2. Epub 2022 Jan 20.