ABSTRACT This paper, applies the concept of KM-fuzzy metric spaces and introduces a novel concept of KM-fuzzy metric hypergraphs based on KM-fuzzy metric spaces. In special cases, we add some conditions to axioms of KM-fuzzy metric hypergraphs(to obtain of elementary hypergraphs, C-accessible hypergraphs, Cor-able hypergraphs, fuzzy hypergraphs) and so obtain locally strong KM-fuzzy metric hypergraphs and strong KM-fuzzy metric hypergraphs. This study, investigates on the finite KM-fuzzy metric spaces with respect to metrics, KM-fuzzy metrics and constructs KM-fuzzy metric spaces on any given non-empty sets. It tries to extend the concept of KM-fuzzy metric spaces to union of KM-fuzzy metric spaces and product of KM-fuzzy metric spaces and in this regard investigates on union and product of KM-fuzzy metric hypergraphs.

ABSTRACT We consider fuzzy inference of the form ‘if ··· then ··· ’. ‘if ··· then ··· else ···’ and ‘if ··· and/or ··· and/or ··· then ···’. Muzomoto proposed logical constructs for ‘if. then.’ by applying Godelien and Standard Sequence methods. These method do not satisfy all the intuitions In this paper, We propose method of fuzzy inference and applied on logical constructs. We have shown that our fuzzy inference method satisfy all the intuitions under several criterions.

Agriculture plays a vital role in Indian economy. On considering the overall geographical space verses population in India, 7% of population is chronicled in Tamilnadu, with 3% of water and 4% of land resources. Thus an automated prediction system becomes essential for predicting the crop based on the nutritional security of the country. In this paper, effort has been made to process the uncertainties by hybridizing rough set on intuitionistic fuzzy approximation space (RSIFAS) [Acharjya DP, Tripathy BK. Rough sets on intuitionistic fuzzy approximation spaces and knowledge representation. Int J Artif Int Comput Res. 2009;1 (1):29–36.] and neural network [Hecht NR. Theory of the backpropagation neural network. Proceedings of the international Joint Conference on neural networks, 1 (1989), 593–605.]. RSIFAS identifies the almost indiscernibility among the natural resources, and helps in reducing the computational procedure on employing data reduction techniques whereas neural network helps in prediction process. It helps to find the crops that may be cultivated based on the available natural resources. The proposed model is analyzed on data accumulated from Vellore district of Tamilnadu, India and achieved 93.7% of average classification accuracy. The model is compared with earlier models and found 6.9% better accuracy while prediction.

From the first issue of 2015 to the third issue of 2018, the journal published 116 papers, all of which were indexed by the core collection of Web of Sciences with an index rate of 100%. Among thes...

In this paper, we propose new notions of fuzzy soft algebras like fuzzy soft ρ-subalgebra (FSρ-SA), fuzzy soft ρ-ideal (FSρ-I), and fuzzy soft . Next, we investigated their relations with other types of fuzzy soft algebras like fuzzy soft d-subalgebra (FSd-SA), fuzzy soft d-ideal (FSd-I), fuzzy soft BCK-subalgebra (FSBCK-SA), fuzzy soft BCK-ideal (FSBCK-I), and others. In addition, we study and discuss some basic characterisations of fuzzy soft ρ-ideal with their applications. Furthermore, several examples are presented to expound our notions in this work.

ABSTRACTIn this paper, we consider a Fuzzy Stochastic Linear Fractional Programming problem (FSLFP). In this problem, the coefficients and scalars in the objective function are the triangular fuzzy number and technological coefficients and the quantities on the right side of the constraints are fuzzy random variables with the specific distribution. Here we change an FSLFP problem to an equivalent deterministic Multi-objective Linear Fractional Programming (MOLFP) problem. Then by using Fuzzy Mathematical programming approach transformed MOLFP problem is reduced single objective Linear programming (LP) problem. A numerical example is presented to demonstrate the effectiveness of the proposed method.

ABSTRACTIn this study, the application of type-II fuzzy sets is addressed to design a multi-product, multi-level, multi-period supply chain networks. The proposed model provides integrated approach to make optimal decisions such as location–allocation, production, procurement and distribution subject to operational and tactical constraints. In the context of fuzzy linear programming, this study involves type-II fuzzy numbers for the right-hand side of constraints regarding three sources of uncertainty: demand, manufacturing and supply. According to fuzzy components considered, a type-II fuzzy mixed-integer linear programming is converted into an equivalent auxiliary crisp model using linear fuzzy type-reducer models. The final models are linear and the global optimum solutions can be achieved using commercial OR softwares. The contributions of this study are three folds: (1) introducing a new integrated supply chain network design problem; (2) considering a solution procedure based on type-II fuzzy sets and (3) presenting a linear fuzzy type-II reducer. Finally, the proposed model and solution approach are illustrated through a numerical example to demonstrate the significance.