A Supplier Selection Model Using the Triangular Fuzzy-Grey Numbers

Zakeri, Shervin; Konstantas, Dimitri; Bratvold, Reidar Brumer; Pamucar, Dragan

doi:10.1109/access.2023.3320032

Cited by 5 publications

(1 citation statement)

References 64 publications

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“…In solving linear programming problems, Ammar [36] suggested an algorithm for solving triangular fuzzy rough integer programming problems; Ghoushchi [37] constructed a method to solve fully fuzzy linear programming based on alpha-cut theory and modified TFN. In addition, scholars have combined fuzzy dispersion measures [38], gray degrees [39], etc. with TFN and innovated TFN.…”

Section: Of 20mentioning

confidence: 99%

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

Xu,

Wang,

Kang

et al. 2024

Sustainability

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By choosing the right pavement maintenance plan, we can reduce resource utilization, reduce environmental pollution, and extend road life, which is important for improving social sustainability. At present, the selection of road maintenance programs mostly adopts multiple attribute decision-making (MADA), in particular, the analytic hierarchy process (AHP) is often used. However, this method needs to use expert scoring data, which leads to strong subjectivity and poor reliability. Therefore, it reduces the science of road maintenance scheme selection. In order to reduce the subjectivity of the score and obtain a more suitable road maintenance scheme, this paper applies a multi-criteria decision-making method that characterizes attribute information by triangular fuzzy numbers (TFN) in the discrete decision space. Firstly, we invite experts to score the importance of the selection of pavement preventive maintenance technical solutions with respect to the indicators affecting the selection of solutions. Secondly, the two indicators of similarity and reliability are used to quantitatively evaluate the indicators and programs, respectively. Finally, we compare the weighted programs according to the overall possibility degree of each program. In actual cases, the overall possibility degree of each scheme obtained by this method is 1.0002–0.0477, and the optimal solution is fog sealing technology. The decision-making model applied in this paper can be considered in multiple dimensions, which can scientifically reduce the subjectivity of expert scoring. The best maintenance plan can also be quickly obtained through the simple calculation method in this paper.

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Section: Of 20mentioning

confidence: 99%

Multi-Attribute Decision-Making Method in Preventive Maintenance of Asphalt Pavement Based on Optimized Triangular Fuzzy Number

Xu,

Wang,

Kang

et al. 2024

Sustainability

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Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection

Hezam,

Ali,

Sallam

et al. 2024

Sci Rep

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Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks.

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