LR-Type Intuitionistic Dense Fuzzy Defuzzification Based Decision Support for Earthquake Rescue Robot Selection

Defuzzification method is an important concept that plays a major role in fuzzy decision making process. This method helps in converting fuzzy quantities into crisp values. In this work, a few prominent defuzzification methods (DeM), namely [Formula: see text]-cut, graded mean integration, area compensation, center of sums, center of gravity, center of largest sums, weighted average method and maxima method which is subdivided into first of maxima, last of maxima and mean of maxima methods have been introduced for LR-type intuitionistic trapezoidal dense fuzzy numbers (LR-ITpDFN), where the LR-type fuzzy numbers are used in generalizing the concept of intuitionistic dense fuzzy set. To determine the optimal DeM from the aforesaid methods, a novel integrated fuzzy multi-criteria decision making (F-MCDM) methods namely criteria importance through inter-criteria correlation (CRITIC) and gray relation analysis (GRA) for LR-ITpDFN is utilized. To apply the proposed model within the LR-ITpDF environment, several statistical measures, including variance, standard deviation, covariance, and correlation coefficient, are extended for LR-ITpDFN to determine the weights of the criteria and rank the alternatives. Additionally, the arithmetic operations for LR-ITpDFN are defined to support the methodology. The robot selection problem for earthquake rescue operations is used to address the novel MCDM models, where the results aim to reduce the human death rate in rescue efforts. A comparative analysis of the proposed defuzzification methods (DeM) is conducted, with the ‘BEAR’ robot ranking first among the alternatives. Moreover, the comparative and sensitive analysis are performed to find the quality of the outcome.