Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets

Shabnam Rahnamay Bonab; Gholamreza Haseli; Hamed Rajabzadeh; Saeid  Jafarzadeh Ghoushchi; Mostafa Hajiaghaei-Keshteli; Hana  Tomaskova

doi:10.31181/dmame12012023b

Authors

Shabnam Rahnamay Bonab Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
Gholamreza Haseli Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico https://orcid.org/0000-0002-1180-1933
Hamed Rajabzadeh Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico https://orcid.org/0000-0002-9096-7607
Saeid Jafarzadeh Ghoushchi Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico https://orcid.org/0000-0003-3665-9010
Mostafa Hajiaghaei-Keshteli Tecnologico de MonterreyTecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico https://orcid.org/0000-0002-9988-2626
Hana Tomaskova Faculty of Informatics and Management, University of Hradec Kralove, 50003 Hradec Kralove, Czech Republic https://orcid.org/0000-0003-1744-5826

DOI:

https://doi.org/10.31181/dmame12012023b

Keywords:

Supplier Selection, Sustainability, IoT, Spherical fuzzy sets, Spherical fuzzy sets, Best-Worst Method, TRUST

Abstract

Supplier selection process plays a vital role in supply chain management and is the most important variable in its success. With increasing environmental considerations, organizations must consider sustainability considerations and economic goals to protect the environment. Furthermore, the destructive effects of disruptions on the supply chain performance of companies have prompted organizational experts to pay special attention to the concept of resilience. This study developed an integrated approach based on the extended version of Multi-Criteria Decision-Making (MCDM) methods in a spherical fuzzy (SFS) environment to address sustainable and resilient IoT supplier selection. In the proposed approach, the main criteria (i.e., resilience, and sustainability) have been used in the supplier selection process. Then, these criteria are weighted using the developed SFS-Best-Worst Method (BWM), which reduces uncertainty in pairwise comparisons. In the next step, the 14 selected IoT suppliers are evaluated and prioritized by applying SFS-mulTi-noRmalization mUlti-Distance aSsessmenT (TRUST) that considers a multi-normalization algorithm to reduce subjectivity in normalized data. The results of this study shows that the pollution control and risk-taking sub-criteria are placed in the first and second priorities, respectively. The comparison of the results of the SFS-TRUST with other MCDM methods and sensitivity analysis demonstrates the performance of the proposed approach and its ranking stability in various scenarios.

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