[PDF] Top 20 FGP approach to multi objective quadratic fractional programming problem

... bi-level multi objective (DBL-MOP) problems with a single decision maker at the up- per level and multiple decision makers at the lower ...goal programming for fuzzy multi objective ... See full document

... MOLFP problem such as the coefficients of the objective function and the constraints, may not be precisely known as various factors are associated with ...as Multi Objective Fuzzy Linear ... See full document

... tri- objective quadratic fractional programming problem (TLMOQPP) then these fuzzy objectives are taken as the fuzzy goals and those goals are evaluated as the fractional ... See full document

... fuzzy fractional assignment problems, where the cost of the objective function is expressed as triangular fuzzy number, is ...linear programming of fuzzy fractional assignment problem ... See full document

... LFP problem with interval coefficients in objective function which is based on Charnes and Cooper technique (2012) solved the LFP problem by algebraic approach which depends on the duality ... See full document

... of FGP model (2) is ( U U , V U , … ...each objective function a ® at this point is a ® U , now we will find membership functions as ª ¯ about the point ( U U , V U , … ...the FGP model for second ... See full document

... Rest of the paper is organized in the following way: Section II presents formulation of chance constrained MOLPLFPP. Section III describes the process of transforming chance constraints into equivalent deterministic ... See full document

... parametric approach. Nagarajan et al. 2014 introduced a solution procedure for multi-objective interval STP under a stochastic ...constrained programming for an uncertain STP ... See full document

... bi-level programming problem with multi-objective [2], [11], ...tackling multi-objective fractional programming problems at two ...the fractional ... See full document

... fuzzy programming for two level linear fractional programming ...Parametric multi-level multi-objective fractional programming problem solution is given by ... See full document

... for multi-objective linear fractional programming ...the fractional objective functions and Zimmermann approach is applied to find the fuzzy efficient of ...continuous ... See full document

... A multi-objective linear programming problem is introduced by Chandra Sen [1] and suggests an approach to construct the multi-objective function under the limitation that ... See full document

... TOPSIS approach based on subjective weights and objective ...MODM problem using TOPSIS ...solving multi – level non – linear multi – objective decision making ...TOPSIS ... See full document

... paper, multi- level multi-objective linear plus linear fractional programming problem is ...The objective functions of level decision makers are characterized by linear ... See full document

... an objective variability in the model parameters (Randomness), or a lack of knowledge of the parameter values (epistemic ...linear programming model with single parameter using α -cuts to achieve an ... See full document

... (MLMOQP) problem, the interactive approach through the satisfactoriness concept,ε -constraint method and The Kth best method is ...(MLMOQP) problem according to the minimal satisfactoriness constant ... See full document

... fuzzy programming model with quadratic membership functions has been developed for the solution of a Multi-Objective Transportation ...fuzzy programming approaches exist with various ... See full document

... a multi-objective optimization problem usually has a set of Pareto-optimal solutions, instead of one single optimal solution ...the objective in a multi-objective optimization is ... See full document

... In section 2 we present some basic definitions. In section 3 locking variable is defined and theorems are proved to find optimal solutions. Some rules to reduce the problem size are presented in section 4. A new ... See full document

... As known, the fuzzy set theory was introduced by Zadeh [20] to deal with fuzziness. Up to now, the fuzzy set theory has been applied to broad fields. Fuzzy set theory has to be set up using data which is approximately ... See full document