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To the best of author's knowledge, only three approaches are proposed in the literature to solve fully fuzzy linear fractional transportation problems. (Linear fractional transportation problems in which each known parameter is represented by a fuzzy number.) In this paper, it is pointed out that it is inappropriate to use any of these existing approaches as some mathematical incorrect assumptions are considered in these existing approaches. Also, a new approach (named as Mehar approach) is proposed to solve fully fuzzy linear fractional transportation problems. Furthermore, an exact fuzzy optimal solution of an existing fully fuzzy linear fractional transportation problem is obtained by the proposed Mehar approach. [ABSTRACT FROM AUTHOR]

Mahmoodirad et al. (Soft Comput, 2018. 10.1007/s00500-018-3115-z) proposed an approach for solving fully intuitionistic fuzzy transportation problems (FIFTPs) (transportation problems in which each parameter is represented as a triangular intuitionistic fuzzy number). In this approach, firstly, an unbalanced fully intuitionistic fuzzy transportation problem (FIFTP) is transformed into a balanced FIFTP, and then the intuitionistic fuzzy (IF) optimal solution of the transformed balanced FIFTP is obtained. In this paper, it is shown that Mahmoodirad et al.'s approach fails to transform an unbalanced FIFTP and hence, Mahmoodirad et al.'s approach fails to find the IF optimal solution of unbalanced FIFTPs. It is obvious that to overcome this limitation of Mahmoodirad et al.'s approach, there is need to propose a method to transform an unbalanced FIFTP into a balanced FIFTP. Therefore, in this paper, a new method (named as JMD method) is proposed to transform an unbalanced FIFTP into a balanced FIFTP. [ABSTRACT FROM AUTHOR]

To the best of our knowledge, only the existing approach (Rabiei et al. in Soft Comput 18(10), 2043–2059, 2014) has been proposed to construct a full interval-valued fuzzy linear regression model [a regression model when the observation of the response, independent variables as well as the regression coefficients are triangular interval-valued fuzzy numbers (TIVFNs)]. However, after a deep study, it is observed that a mathematical incorrect assumption has been considered in this approach. Furthermore, it is observed that to resolve this mathematical incorrect assumption, there is a need to propose the multiplication of an unrestricted TIVFN (regression coefficient) with a restricted TIVFN [observed values of independent variable(s)]. Keeping the same in mind, in this paper, the same type of multiplication is proposed, and with the help of proposed multiplication, a modified approach is proposed to construct a full interval-valued fuzzy regression model. Also, the modified results of some existing real-life problems are obtained with the help of the modified approach. [ABSTRACT FROM AUTHOR]

In this paper, we discuss some new numerical methods to solve a fully fuzzy linear system (FFLS) with triangular fuzzy numbers of the form $$ ( {m,\alpha ,\beta }) $$. Almost every existing method that intends to solve a FFLS confines the coefficient matrix and the solutions to be non-negative fuzzy numbers. The main intent of the proposed methods is to remove these restrictions and widen the scope of fuzzy linear systems in scientific applications. The methods are illustrated with the help of numerical examples and are conceptually easy to understand and apply in real life situations. [ABSTRACT FROM AUTHOR]

Ranking of fuzzy numbers play an important role in decision making, optimization, forecasting etc. Fuzzy numbers must be ranked before an action is taken by a decision maker. In this paper, with the help of several counter examples it is proved that ranking method proposed by Chen and Chen (Expert Syst Appl 36:6833-6842, ) is incorrect. The main aim of this paper is to propose a new approach for the ranking of L- R type generalized fuzzy numbers. The proposed ranking approach is based on rank and mode so it is named as RM approach. The main advantage of the proposed approach is that it provides the correct ordering of generalized and normal fuzzy numbers and it is very simple and easy to apply in the real life problems. It is shown that proposed ranking function satisfies all the reasonable properties of fuzzy quantities proposed by Wang and Kerre (Fuzzy Sets Syst 118:375-385, ). [ABSTRACT FROM AUTHOR]