Interactive fuzzy programming for two-level nonconvex nonlinear programming problems through genetic algorithms

In this paper, we propose an interactive fuzzy programming for two-level no nconvex nonlinear problems based on the use of genetic algorithms that have demonstrated their efficiency in solving the nonconvex nonlinear optimizat ion problems. Genetic algorithms have also recently become a widely accepte d method for handling optimization, adaptation, and learning. According to the proposed method, fuzzy goals for the objective functions of decision ma kers at each level are stipulated, after which the decision maker at the up per level subjectively sets the minimum acceptable level of the satisfactio n degrees, while considering the ratio of degrees of satisfaction between t he 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 solu tion of two-level nonconvex nonlinear programming problems using a numerica l example. (C) 2000 Scripta Technica.