Centroid and Graded Mean Ranking Methods for Intuitionistic Trapezoidal Dense Fuzzy Set to Solve MCDM Problems of Robot Selection

Sampathkumar, Swethaa; Augustin, Felix; Narayanamoorthy, Samayan; Ahmadian, Ali; Ferrara, Massimiliano; Kang, Daekook

doi:10.1007/s40815-023-01647-2

Int. J. Fuzzy Syst.

2024

DOI: 10.1007/s40815-023-01647-2

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Centroid and Graded Mean Ranking Methods for Intuitionistic Trapezoidal Dense Fuzzy Set to Solve MCDM Problems of Robot Selection

Swethaa Sampathkumar,

Felix Augustin,

Samayan Narayanamoorthy

et al.

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Cited by 2 publications

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Editorial: Fuzzy Big Data-Driven Computational Intelligence Models and Applications

Li,

Fujita,

Zhang

et al. 2024

Int. J. Fuzzy Syst.

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Editorial: Fuzzy Big Data-Driven Computational Intelligence Models and Applications

Li,

Fujita,

Zhang

et al. 2024

Int. J. Fuzzy Syst.

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An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection

Tran,

Trinh,

Chung

2024

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In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific production process is a complex task. There are numerous criteria to consider, often conflicting with each other, making decision-making challenging. In order to tackle this problem, the multi-criteria decision-making (MCDM) method is employed, which aids in ranking decisions based on criteria weights. However, traditional MCDM methods are now considered outdated, and researchers are concentrating on hybrid models that include multiple MCDM techniques to tackle decision-making problems effectively. This study presents an effective MCDM model that integrates Fuzzy-AHP-TOPSIS to evaluate and choose the best robot. The Fuzzy-AHP is utilized to establish a set of weights for the evaluation criteria. Subsequently, the proposed technique analyzes, prioritizes, and chooses the best robot option from the ranking list for the factory. The experimental results demonstrate that by employing the integrated fuzzy analytical hierarchy process, taking into account parameter weights and expert judgment, the robots are identified in order of best to worst alternatives to factories. The outcomes of this research possess significant implications for robot selection and can be applied in various fields to cater to production requirements.

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Centroid and Graded Mean Ranking Methods for Intuitionistic Trapezoidal Dense Fuzzy Set to Solve MCDM Problems of Robot Selection

Cited by 2 publications

References 34 publications

Editorial: Fuzzy Big Data-Driven Computational Intelligence Models and Applications

Editorial: Fuzzy Big Data-Driven Computational Intelligence Models and Applications

An Integrated Approach of Fuzzy AHP-TOPSIS for Multi-Criteria Decision-Making in Industrial Robot Selection

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