Effect of normalization techniques and criteria weight calculation methodologies on nontraditional machining process rank for m-polar fuzzy set ELECTRE-I algorithm.

The m-polar fuzzy set (mFs) algorithm is a novel approach to solving multi-polar uncertainty problems. Combining other multi-criteria decision-making techniques can improve this algorithm in efficient ways. This work examines the ELimination Et Choice Translating Reality-I (ELECTRE-I) approach. The input data given to any MCDM technique will change the rank of alternatives based on the method of normalization used and the weight calculation method utilized for the criteria weight calculation purpose. Various normalization approaches and criteria weight methods are required to transform real data into normalized data for MCDM method implementation. This study uses a framework to implement the mFs ELECTRE-I algorithm to investigate the effect of AHP weight and Shannon's entropy weight (SEW) calculation combined with normalization strategies. In this paper, selecting nontraditional machining (NTM) processes is evaluated to assess the impact of the created framework on choices and rank performance analysis. The study results in linear sum and vector normalization are suitable normalization techniques with AHP as well as Shannon's entropy weight (SEW) criteria weight methodology with Spearman's rank correlation coefficients as 0.8333 and 0.9285, respectively, which shows consistency of the rank of alternatives with the results obtained by previous researchers. This study can be used to select suitable normalization and criteria weight methods for the m-polar fuzzy set ELECTRE-I algorithm while solving multi-criteria decision-making problems. The framework is intended to address various industrial problems with many criteria, including the selection of robots, non-traditional machining techniques, and various industrial challenges without considering their subgroups. [ABSTRACT FROM AUTHOR]