This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New ex...

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New ex...

using the mathematic techniques such as fuzzy approach has useful outcomes for production planning in different sources. in this paper lgp1 was used to model the objectives such as: avoidance of shortage or surplus of demand, access to maximum of income, using the normal capacity of production and organizing the inventory of warehouse, within the framework of goal constraints like balancing be...

This paper introduces a computational method of solving fully fuzzy multi objective linear programming problem through goal programming approach. Here we deal the imprecise parameters as fuzzy numbers with assumption that these fuzzy numbers have some possibility distribution associated with fuzzy variables. In the study, we extend the concept of conflict and non-conflict between objective func...

Several tools are used to develop expert systems with the help of either programming or developing a special shell. A good expert system shell is projected to hold uncertainty properly for use in attractive domains of applications that deal with imprecise information. In this paper, we have implemented and tested a shell using new inference methods with the support of fuzzy logic to achieve our...

This paper presents a fuzzy goal programming (FGP) methodology for solving bi-level quadratic programming (BLQP) problems. In the FGP model formulation, firstly the objectives are transformed into fuzzy goals (membership functions) by means of assigning an aspiration level to each of them, and suitable membership function is defined for each objectives, and also the membership functions for vec...

the fuzzy linear regression model with fuzzy input-output data andcrisp coefficients is studied in this paper. a linear programmingmodel based on goal programming is proposed to calculate theregression coefficients. in contrast with most of the previous works, theproposed model takes into account the centers of fuzzy data as animportant feature as well as their spreads in the procedure ofconstr...