Fuzzy set theory has been applied to many fields, such as operations research, control theory, and management sciences. We consider two classes of fuzzy linear programming (FLP) problems: Fuzzy number linear programming and linear programming with trapezoidal fuzzy variables problems. We state our recently established results and develop fuzzy primal simplex algorithms for solving these problem...

The reason for using fuzzy logic in control applications stems from the idea of modeling uncertainties in the knowledge of a system’s behavior through fuzzy sets and rules that are vaguely or ambiguously specified. By defining a system’s variables as linguistic variables such that the values they can take are also linguistic terms (modeled as fuzzy sets), and by establishing the rules based on ...

Abstract This paper presents a new complex system systemic. Here, we are working in a fuzzy environment, so we have to adapt all the previous concepts and results that were obtained in a non-fuzzy environment, for this fuzzy case. The direct and indirect influences between variables will provide the basis for obtaining fuzzy and/or non-fuzzy relationships, so that the concepts of coverage and i...

A simple and e!ective method for selecting signi"cant input variables and determining optimal number of fuzzy rules when building a fuzzy model from data is proposed. In contrast to the existing clustering-based methods, in this approach both input selecting and partition validating are determined on the basis of a class of sub-clusters created by a self-organising network instead of on the dat...

The COCOMO Model is well known as the currently predominate model for software cost estimation. It allows one to work from linguistic variables to estimate software project effort and schedule. This basis in linguistic variables encourages research of the COCOMO Model as a fuzzy system. As is known in fuzzy circles and is shown here, fuzzy arithmetic based on the popular fuzzy extension princip...

Statistical data are frequently not precise numbers but more or less non-precise, also called fuzzy. Measurements of continuous variables are always fuzzy to a certain degree. Therefore histograms and generalized classical statistical inference methods for univariate fuzzy data have to be considered. Moreover Bayesian inference methods in the situation of fuzzy a-priori information and fuzzy da...

This work aims to improve simulation performance by introducing fuzzy logic in the discrete event simulation. This paper attempts to develop a new approach to assess the duration of dynamical systems, especially the natural processes. To do, we have used the fuzzy logic theory and particularly the fuzzy inference system combined to the discrete event simulation (DEVS). The process simulation is...

Correct scheduling of the project is the necessary condition for the project success. In traditional models, the activities duration times are deterministic and known. In real world however, accurate calculation of time for performing each activity is not possible and is always faced with uncertainty. In this paper, the duration of each activity is estimated by the experts as linguistic variabl...

A new method namely, bound and decomposition method is proposed to find an optimal fuzzy solution for fully fuzzy linear programming (FFLP) problems. In the proposed method, the given FFLP problem is decomposed into three crisp linear programming (CLP) problems with bounded variables constraints, the three CLP problems are solved separately and by using its optimal solutions, the fuzzy optimal ...

This paper proposes a fuzzy discriminant analysis to solve the two-group classification problem where the measured variables are linguistic in nature. Especially under imprecise framework, the linguistic variables capture more information although vagueness is inherent. In analogy to classical statistics, a fuzzy linear discriminant function is introduced here, which directly deals with continu...