Fuzzy Computing in Data Science

7Modified m-Polar Fuzzy Set ELECTRE-I Approach

Madan Jagtap¹*, Prasad Karande² and Pravin Patil²

¹ Mechanical Engineering Department, SCOE, Kharghar, Navi Mumbai, India

² Mechanical Engineering Department, VJTI, Matunga, Mumbai, India

Abstract

Fuzzy sets help in dealing with the vagueness in data. An m-polar fuzzy set extends the set where a pole describes the relevance to the related property that controls the system. Since 2014, the m-polar fuzzy set is being used as a decision-making tool, and day by day, its usefulness is increasing. The challenge arises in using criteria weights while implementing m-polar fuzzy set hybrid methods. Researchers have either considered criteria weights directly or assumed their values. Thus, there is a need to obtain criteria weight while implementing various m-polar fuzzy set hybrid methodologies. For example, one can implement m-polar fuzzy set hybrid methods with AHP or Shannon’s entropy weight calculation approach. In this chapter, Shannon’s entropy weight calculation integrated m-polar fuzzy ELECTRE-I methodology is developed for solving a single-pole (single-valued) MCDM problem. Two industrial selection problems, i.e., selection of flexible manufacturing system and selection of cutting fluid, are considered for demonstration purposes. Work validation is done with a comparison of rankings of alternatives with earlier rankings. It is observed that Shannon’s entropy weight calculation integrated m-polar fuzzy ELECTTRE-I concept is consistent ...