Quadratic programming (QP) is an optimization problem wherein one minimizes (or maximizes) a quadratic function of a finite number of decision variable subject to a finite number of linear inequality and/ or equality constraints. In this paper, a quadratic programming problem (FFQP) is considered in which all cost coefficients, constraints coefficients, and right hand side are characterized by ...

abstract regarding the imprecise or fuzzy data on the agriculture activities, applying models based on fuzzy data may be useful. in line with this condition this study tries to compare the ability of possiblistic programming approach in developing optimal solution with fuzzy and deterministic programming. to get this objective a data set obtained from 90 farmers of kohgilooye region, gathered i...

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...

in this article, an effective method to control a power system during emergency conditions is presented. based on fuzzy linear programming (flp), a new technique is developed to solve the load shedding and generation reallocation (lsgr) optimization problem. the objective function consists of terms of load curtailments and deviations in generation schedules. the constraints are power system var...

This paper proposes a compromise model, based on a new method, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. In this compromise programming method, two concepts are considered simultaneously. First of them is that the optimal ...

We present a logical framework to represent and reason about fuzzy optimization problems based on fuzzy answer set optimization programming. This is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and maximum in the language of fuzzy answer set optimization programming to allow minimization or maximization of some desired criteria under fuzzy environments. We show the appl...

Abstract In this paper, the notions of subgradmnt, subdifferentla[, and differential with respect to convex fuzzy mappings are investigated, whmh provides the basis for the fuzzy extremum problem theory We consider the problems of minimizing or maximizing a convex fuzzy mapping over a convex set and develop the necessary and/or sufficient optlmahty conditions. Furthermore, the concept of saddle...

Three types of fuzzy random programming for capacitated location-allocation problem with fuzzy random demands are proposed according to different criteria, including the expected value model, chanceconstrained programming and dependent-chance programming. Finally, integrating the network simplex algorithm, fuzzy random simulation and genetic algorithm, a hybrid intelligent algorithm is produced...

Several authors have used ranking function for solving fuzzy linear programming problems. In this paper some fuzzy linear programming problems are chosen which can’t be solved by using any of the existing methods and a new method is proposed to solve such type of fuzzy linear programming problems. The main advantage of the proposed method over existing methods is that the fuzzy linear programmi...