Implementing Combinative Distance Base Assessment (CODAS) for Selection of Natural Fibre for Long Lasting Composites

Wankhede, Sagar V.; Pesode, Pralhad; Gaikwad, Sanjay; Pawar, Sumod; Chipade, Amar

doi:10.4028/p-4pd120

Cited by 10 publications

(1 citation statement)

References 24 publications

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“…Remadi & Frikha 88 proposed a model for ranking green materials using CODAS method and utilized intuitionistic fuzzy sets within an uncertain group decision-making environment. Wankhede et al 89 focused on selection of natural fiber for long lasting composites using CODAS method. Basalt was found as the best natural for long lasting composites followed by flax and Kenaf respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A decision analysis model for material selection using simple ranking process

Zakeri

Chatterjee

Konstantas

et al. 2023

Sci Rep

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A large number of materials and various criteria fashion material selection problems as complex multi-criteria decision-making (MCDM) problems. This paper proposes a new decision-making method called the simple ranking process (SRP) to solve complex material selection problems. The accuracy of the criteria weights has a direct impact on the outcomes of the new method. In contrast to current MCDM methods, the normalization step has been eliminated from the SRP method as a potential source of producing incorrect results. The application of the method is appropriate for situations with high levels of complexity in material selection because it only considers the ranks of alternatives in each criterion. The first scenario of vital-immaterial mediocre method (VIMM) is used as a tool to derive criteria weights based on expert assessment. The result of SRP is compared with a number of MCDM methods. In order to evaluate the findings of analytical comparison, a novel statistical measure known as compromise decision index (CDI) is proposed in this paper. CDI revealed that the MCDM methods’ outputs for solving the material selection could not be theoretically proven and requires to be evaluated through practice. As a result, the dependency analysis-an additional innovative statistical measure is introduced to demonstrate the reliability of MCDM methods by assessing its dependency on criteria weights. The findings demonstrated that SRP is extremely reliant on criteria weights and its reliability rises with the number of criteria, making it a perfect tool for solving challenging MCDM problems.

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Section: Literature Reviewmentioning

confidence: 99%