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This paper presents how the stochastic simulation based genetic algorithm (GA) can be used to the fuzzy goal programming (FGP) formulation of a chance constrained multiobjective decision making (MODM) problem. In the proposed approach, a stochastic simulation to the chance constraints having the continuous random parameters is introduced first to determine the candidate solutions in the decision making...
This paper presents how genetic algorithm (GA) can be used in fuzzy goal programming (FGP) formulation of multiobjective stochastic programming (SP) problems. In the proposed approach, the individual optimal decision of each of the objectives are determined by using the GA scheme adopted in the process of solving the problem after converting the chance constraints into their deterministic equivalent...
This paper presents a genetic algorithm (GA) based fuzzy goal programming (FGP) solution method to multiobjective decision making (MODM) problems with fractional criteria. In the model formulation of the problem, first fractional objectives are transformed into fuzzy goals by defining the imprecise aspiration levels to each of them by employing the proposed GA. Then, the concept of membership functions...
In this article, the efficient use of a genetic algorithm (GA) to the goal programming (GP) formulation of interval valued multiobjective fractional programming problems (MOFPPs) is presented. In the proposed approach, first the interval arithmetic technique [1] is used to transform the fractional objectives with interval coefficients into the standard form of an interval programming problem with...
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