Chebyshev Distance Entropy Combined with TODIM Method in Lq-ROFS Multiattribute Group Decision Making

Table 1

Benefits and weaknesses of existing methods.

Methods	Environment	Benefits	Weaknesses	Targeted issues
MABAC [62, 63]	Triangular fuzzy number	Condition stability	The linguistic terms are not accurate enough when converted to real numbers	MCDM
RAFSI [64]	Interval number	Transfer data into interval, eliminates the rank reversal problem	Less of objective and subjective criteria weighting techniques	MADM
MAIRCA [65, 66]	Interval rough number	High stability	Lack of model of determining the weight coefficients of criteria	MCDM
MARCOS [67]	Real number	Flexibility, less affected by increases in attributes or criteria	The scale of five degrees is not accurate enough to describe the degree	MCDM
LMAW [68]	Triangular fuzzy number	Conducted practice	The quality of this method cannot be determined in uncertain field	MCDM
TOPSIS [69]	Lq-ROFS	The linguistic scale function is proposed in the method for decision making	Have no weight calculation model	MAGDM
LqROFWA	Lq-ROFS	Easy to use and understand	Do not consider the partition of input values	MAGDM
LqROFIWPGHM	Lq-ROFS	Consider the interactions between the MD and NMD	Using hamming distance	MAGDM
LqROFWG	Lq-ROFS	Easy to use and understand	Do not consider the inter-relationships between input values	MAGDM
Fuzzy measures and choquet integrals [70]	Rough	Simplify the process of MCGDM and remove the redundant data for the sort result	Have no model to solve for criteria weight	MCDM
WAGE [71]	Grey	Consider multiple attributes simultaneously	The AHP method for solving the weights is too subjective	MCDM