IMPROVED FUZZY SET THEORY FOR SOLVING MULTI CRITERIA DECISION MAKING PROBLEMS IN PRODUCT DESIGN

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Mr.Prakash Bapurao Fulari, Dr. Dilip Bapurao Ghule, Dr. VineetaBasotia

Abstract

Multi Criteria Decision Making (MCDM)aims to provide decision forproblems under complex
decision making environmentwhich considered as critical task in various applications such as product design,
service provider selection and quality selection.In literature, some efficient techniques are available to find the
optimal selection with respect to the criteria. These methods are fails to attain high selection accuracy due to
increased cost, unwanted data entry, low quality of product and wastage of time. Hence, in this proposed
methodology, improved Hierarchical-Fuzzy (H-Fuzzy)set theory is designed to solve the MCDM problems in
product design applications. The proposed H-Fuzzy theory is used to optimal selection of product tool selection
by using normalized average weight gain operation. The proposed H-Fuzzy set theory is working based on two
main steps such as priority weighting and normalized weighting. The priority weighting is achieved in H-Fuzzy
theory and the overall priority weights alternatives determined. Based on these overall priority weights, the
alternatives are ranked. The ranking process is considered as final step of proposed methodology which selected
the optimal product tool. The proposed H-Fuzzy set theory is implemented in a MATLAB platform and its
performances were evaluated. The statistical measurements are considered to analysis the performance of the
proposed methodology such as accuracy, sensitivity, specificity, and kappaand error measurement. The outcome
shows that the proposed model is better than the previous ones.

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How to Cite
Mr.Prakash Bapurao Fulari, Dr. Dilip Bapurao Ghule, Dr. VineetaBasotia. (2021). IMPROVED FUZZY SET THEORY FOR SOLVING MULTI CRITERIA DECISION MAKING PROBLEMS IN PRODUCT DESIGN. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(14), 2749–2762. https://doi.org/10.17762/turcomat.v12i14.10770
Section
Research Articles