@tehattagovtcollege.ac.in
Assistant Professor, Department of Mathematics
Tehatta Government College
Just a researcher...
PhD, Msc, Bsc
Multidisciplinary, Mathematics, Computational Mathematics
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Rupak Bhattacharyya and Supratim Mukherjee
Tsinghua University Press
ABSTRACT In most researches on fuzzy sets and its application, it is found that the consideration of membership function is predetermined and mostly linear in nature. Extraction and evaluation of non-linear fuzzy membership function that can update itself with in different paradigms is still a matter of great concern to researchers. Here, we discuss 33 different membership function evaluation methodologies published between 1971 and 2016. In a approach to solve the problem, this paper presents a novel algorithm based non-linear fuzzy membership function evaluation scheme with the help of regression analysis and algebra. Three different case studies are done to check the applicability and tractability of the method. A comparative analysis with recent literature justifies the robustness of the proposed method.
A. Chatterjee, S. Mukherjee, and S. Kar
Tsinghua University Press
ABSTRACT Nowadays, supplier selection process, a multicriteria decision-making problem, has become one of the most indispensable parts for every purchasing sector for the improvement of performances of business operations. Most of the literatures in this field have considered only the opinion of decision-makers. But in fact, each company has its own opinion about the suppliers. The purpose of this paper is to select the best supplier by integrating the opinions of both decision- makers and company's stake holders. In this literature, these opinions are taken as fuzzy soft sets. These two fuzzy soft sets are then integrated by the rough approximation theory. The attributes in this literature are taken in the form of linguistic variable. At the end of this paper, a case study is given to illustrate the proposed method for selecting the best supplier.
Amitava Chatterjee, Supratim Mukherjee, and Samarjit Kar
Tsinghua University Press
Supratim Mukherjee, Rupak Bhattacharyya, Amitava Chatterjee, Samarjit Kar, Swapan Paruya, Samarjit Kar, and Suchismita Roy
AIP
Co‐curricular activities have a great importance in students’ life, especially to grow their personality and communication skills. In different process of evaluating competitors in such competitions, generally crisp techniques are used. In this paper, we introduce a new fuzzy set theory based method of evaluation of competitors in co‐curricular activities like debate and recitation competitions. The proposed method is illustrated by two examples.
Amitava Chatterjee, Rupak Bhattacharyya, Supratim Mukherjee, Samarjit Kar, Swapan Paruya, Samarjit Kar, and Suchismita Roy
AIP
The purpose of the paper is to construct a mean‐semivariance‐skewness portfolio selection model in fuzzy random environment. The objective is to maximize the skewness with predefined maximum risk tolerance and minimum expected return. Here the security returns in the objectives and constraints are assumed to be fuzzy random variables in nature and then the vagueness of the fuzzy random variables in the objectives and constraints are transformed into fuzzy variables which are similar to trapezoidal numbers. The newly formed fuzzy model is then converted into a deterministic optimization model. The feasibility and effectiveness of the proposed method is verified by numerical example extracted from Bombay Stock Exchange (BSE). The exact parameters of fuzzy membership function and probability density function are obtained through fuzzy random simulating the past dates.
Dwijesh Dutta Majumder, Rupak Bhattacharyya, and Supratim Mukherjee
IEEE
In fuzzy mathematics, evaluation of membership function is still a problem, as the methods for this purpose do not hold well in all aspects. The purpose of this work is to assemble and to draw an overview of them. In addition, this work consists of a new approach, which may lead to a new way. The approach is from numerical point of view with the help of statistics. There are two methods, namely (i) modified Newton's divided difference method and (ii) modified Lagrange's interpolation method