This paper considers multiobjective linear programming problems (MOLPP) where random fuzzy variables are contained in objective functions and constraints. A new decision making model optimizing possibilistic value at risk (pVaR) is proposed by incorporating the concept of value at risk (VaR) into possibility theory. It is shown that the original MOLPPs involving random fuzzy variables are trans...

Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagation learning algorithm are not tailored to this kind of classification problem. Hence, in this paper, feedforward neural networks, that use backpropagation learning algorithm with fuzzy objective functions, are investigated. A learning al...

The purpose of the current research is to provide a performance appraisal system capable of considering the value chain network structure of research and development (R&D) projects for Complex products and systems (CoPS) under uncertainty of data. Therefore, in order to achieve this goal, a network data envelopment analysis (NDEA) approach and the possibilistic programming to provide a new fuzz...

The conventional wisdom is that the concept of information is closely related to the concept of probability. In Shannon's information theory, information is equated to a reduction in entropy—a probabilistic concept. In this paper, a different view of information is put on the table. Information is equated to restriction. More concretely, a restriction is a limitation on the values which a varia...

Usage of fuzzy differential equations (FDEs) is a natural way to model dynamical systems under possibilistic uncertainty. We consider second order hybrid fuzzy differentia

In this paper, possibilistic linear programming problems are investigated. After reviewing relations among conjunction and implication functions, necessity fractile optimization models with various implication functions are applied to the possibilistic linear problems. We show that the necessity fractile optimization models are reduced to semi-infinite linear programming problems. A simple nume...

Classical ontologies are not suitable to represent imprecise nor uncertain pieces of information. As a solution we will combine fuzzy Description Logics with a possibilistic layer. Then, we will show how to perform reasoning by relying on classical existing reasoners. Description Logics (DLs for short) are a family of logics for representing structured knowledge which have proved to be very use...

This paper discuss mainly issues related for modeling decision making under uncertain, vagueness, risky and imprecise information. There will be presented a description of five ordinal methods for modeling decision making under uncertainty in the context of linguistic data: Possibilistic Decisisonmaking, Revised Possibilistic Decisisonmaking, Commensurate L-Fuzzy Risk Minimization, Fuzzy relati...

Product planning is one of four important processes in new product development (NPD) using quality function deployment (QFD), which is a widely used customer-driven approach. In our opinion, the first problem to be solved is how to incorporate both qualitative and quantitative information regarding relationships between customer requirements (CRs) and engineering characteristics (ECs) as well a...

The study of fuzzy intervals is of particular interest in temporal database research. In order to optimize the storage of fuzzy temporal intervals, some transformations have been proposed. In this paper we analyze the possibilistic evaluation of the ill-known temporal intervals. We propose a framework to deal with the evaluation of ill-known temporal intervals. It is shown how the reasoning beh...