[naeini1] in this paper, a new method is proposed to find the fuzzy optimal solution of fuzzy multi-objective linear programming problems (fmolpp) with fuzzy right hand side and fuzzy decision variables. due to the imprecise nature of available resources, determination of a definitive solution to the model seems impossible. therefore, the proposed model is designed in order to make fuzzy decisi...

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at the object-language level. In spite of its expressive power, an important limitation in P-DeLP is that imprecise, fuzzy information cannot be expressed in the object language. One ...

In this paper, a possibilistic disjunctive logic programming approach for modeling uncertain, incomplete and inconsistent information is defined. This approach introduces the use of possibilistic disjunctive clauses which are able to capture incomplete information and incomplete states of a knowledge base at the same time. By considering a possibilistic logic program as a possibilistic logic th...

Compared with the conventional probabilistic mean-variance methodology, fuzzy number can better describe an uncertain environment with vagueness and ambiguity. Based on this fact, possibilistic mean-variance utilities to portfolio selection for bounded assets are discussed in this paper. The possibilistic mean value of the expected return is termed measure of investment return and the possibili...

This article considers bilevel linear programming problems where the coefficients of the objective functions in the problem are given as a possibilistic variable characterized by a quadratic membership function. An extended Stackelberg solution is defined by incorporating the notions of possibility theory into the original concept of Stackelberg solutions. The characteristic of the proposed mod...

The objective of the paper is to deal with a kind of possibilistic linear programming (PLP) problem involving multiple objectives of conflicting nature. In particular, we have considered a multi objective linear programming (MOLP) problem whose objective is to simultaneously minimize cost and maximize profit in a supply chain where cost and profit coefficients, and related parameters such as av...

Classical stochastic Markov Decision Processes (MDPs) and possibilistic MDPs ( -MDPs) aim at solving the same kind of problems, involving sequential decision making under uncertainty. The underlying uncertainty model (probabilistic / possibilistic) and preference model (reward / satisfaction degree) change, but the algorithms, based on dynamic programming, are similar. So, a question maybe rais...

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at object-language level. This paper presents a first approach towards extending P-DeLP to incorporate fuzzy constants and fuzzy propositional variables. We focus on how to characteri...