In this paper, we propose an interactive fuzzy programming for two-levelnonconvexnonlinear problems based on the use of genetic algorithms that have demonstrated their efficiency in solving the nonconvexnonlinear o...
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In this paper, we propose an interactive fuzzy programming for two-levelnonconvexnonlinear problems based on the use of genetic algorithms that have demonstrated their efficiency in solving the nonconvexnonlinear optimization problems. Genetic algorithms have also recently become a widely accepted method for handling optimization, adaptation, and learning. According to the proposed method, fuzzy goals for the objective functions of decision makers at each level are stipulated, after which the decision maker at the upper level subjectively sets the minimum acceptable level of the satisfaction degrees, while considering the ratio of degrees of satisfaction between the levels, and interactively updates, if necessary, the minimum acceptable levels of the decision makers to efficiently obtain a satisfactory solution by establishing a balance of satisfaction degrees between the levels while respecting the motivations of the upper-level decision maker. Finally, we demonstrate the adequacy and efficiency of the proposed method for the solution of two-level nonconvex nonlinear programming problems using a numerical example. (C) 2000 Scripta Technica.
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