SUSTAINABLE RESILIENT SUPPLIER SELECTION FOR IOT IMPLEMENTATION BASED ON THE INTEGRATED BWM AND TRUST UNDER SPHERICAL FUZZY SETS

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020787" target="_blank" >RIV/62690094:18450/23:50020787 - isvavai.cz</a>

Výsledek na webu

<a href="https://dmame-journal.org/index.php/dmame/article/view/584" target="_blank" >https://dmame-journal.org/index.php/dmame/article/view/584</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31181/dmame12012023b" target="_blank" >10.31181/dmame12012023b</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SUSTAINABLE RESILIENT SUPPLIER SELECTION FOR IOT IMPLEMENTATION BASED ON THE INTEGRATED BWM AND TRUST UNDER SPHERICAL FUZZY SETS

Popis výsledku v původním jazyce

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 mUltiDistance 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. © 2023 by the authors.

Název v anglickém jazyce

SUSTAINABLE RESILIENT SUPPLIER SELECTION FOR IOT IMPLEMENTATION BASED ON THE INTEGRATED BWM AND TRUST UNDER SPHERICAL FUZZY SETS

Popis výsledku anglicky

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 mUltiDistance 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. © 2023 by the authors.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Decision Making: Applications in Management and Engineering

ISSN

2560-6018

e-ISSN

2620-0104

Svazek periodika

6

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

33

Strana od-do

153-185

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85159366943