A novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection problem

Research areas:

• Applications - Economy

Year:

2020

Type of Publication:

Article

Keywords:

Dombi aggregators, fuzzy decision making, MABAC, neutrosophic sets, resilient supply chain

Authors:

• Pamucar, Dragan
• Yazdani, Morteza
• Obradovic, Radojko
• Kumar, Anil
• Torres-Jiménez, Mercedes

Journal:

International Journal of Intelligent Systems

Volume:

35

Number:

12

Pages:

1934-1986

Month:

December

ISSN:

0884-8173

BibTex:

@article{Pamucar2020, author = "Dragan Pamucar and Morteza Yazdani and Radojko Obradovic and Anil Kumar and Mercedes Torres-Jim{\'e}nez", abstract = "The objectives of this study are to mitigate the risk and disturbances to the supply chain, to offer required models for resolving the complex issues that arise, and to maintain the stability of the support system. Also, the uncertain conditions in a supply chain force decision‐makers and experts to adopt a fuzzy‐based evaluation platform to ensure secure and reliable consequences. The current study proposed a fuzzy neutrosophic decision‐making approach for supplier evaluation and selection. The model is composed of a new weight aggregator that uses pairwise comparison, which has not been reported to date. The model uses a Dombi aggregator that is more qualified than other aggregators. The Dombi t‐conorms and t‐norms have the same properties as those of the general t‐conorm and t‐norm, which can enhance the flexibility of the information aggregation process via the adjustment of a parameter. A decision‐making environment with uncertain conditions and multiple factors is supposed. We applied this approach in a construction company to analyse the suppliers in a resilient supply chain management (RSCM) system using a MABAC (multiattribute border approximation area comparison) tool. The accuracy of the proposed model was examined via sensitivity analysis tests. This study proposes a novel fuzzy‐neutrosophic‐based approach for resilient supplier selection. The main contributions of this study work are the design, implementation and analysis of a multiattribute evaluation system with respect to fuzzy neutrosophic values. In this evaluation system, a new pairwise comparison is conducted with trapezoidal neutrosophic linguistic variables to determine the importance weights of supplier criteria. Typically, the provision of opinions regarding the qualitative performances of suppliers is a difficult and confusing responsibility for experts and supplier evaluators. Therefore, the proposed approach overcomes this problem by utilizing a pairwise comparison by neutrosophic values and proposes original Dombi aggregation operators for dealing with fuzzy neutrosophic sets.", awards = "JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", comments = "JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", doi = "10.1002/int.22279", issn = "0884-8173", journal = "International Journal of Intelligent Systems", keywords = "Dombi aggregators, fuzzy decision making, MABAC, neutrosophic sets, resilient supply chain", month = "December", note = "JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.", number = "12", pages = "1934-1986", title = "{A} novel fuzzy hybrid neutrosophic decision‐making approach for the resilient supplier selection probl", url = "doi.org/10.1002/int.22279", volume = "35", year = "2020", }

Note:

JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.

Abstract:

The objectives of this study are to mitigate the risk and disturbances to the supply chain, to offer required models for resolving the complex issues that arise, and to maintain the stability of the support system. Also, the uncertain conditions in a supply chain force decision‐makers and experts to adopt a fuzzy‐based evaluation platform to ensure secure and reliable consequences. The current study proposed a fuzzy neutrosophic decision‐making approach for supplier evaluation and selection. The model is composed of a new weight aggregator that uses pairwise comparison, which has not been reported to date. The model uses a Dombi aggregator that is more qualified than other aggregators. The Dombi t‐conorms and t‐norms have the same properties as those of the general t‐conorm and t‐norm, which can enhance the flexibility of the information aggregation process via the adjustment of a parameter. A decision‐making environment with uncertain conditions and multiple factors is supposed. We applied this approach in a construction company to analyse the suppliers in a resilient supply chain management (RSCM) system using a MABAC (multiattribute border approximation area comparison) tool. The accuracy of the proposed model was examined via sensitivity analysis tests. This study proposes a novel fuzzy‐neutrosophic‐based approach for resilient supplier selection. The main contributions of this study work are the design, implementation and analysis of a multiattribute evaluation system with respect to fuzzy neutrosophic values. In this evaluation system, a new pairwise comparison is conducted with trapezoidal neutrosophic linguistic variables to determine the importance weights of supplier criteria. Typically, the provision of opinions regarding the qualitative performances of suppliers is a difficult and confusing responsibility for experts and supplier evaluators. Therefore, the proposed approach overcomes this problem by utilizing a pairwise comparison by neutrosophic values and proposes original Dombi aggregation operators for dealing with fuzzy neutrosophic sets.

Comments:

JCR(2020): 8.709 Position: 12/139 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE.