A genetic-fuzzy procedure for solving fuzzy multiresponses problem
genetic-fuzzy procedure for solving fuzzy multiresponses problem
AbstractThis research proposed a genetic-fuzzy procedure to enhance fuzzy multiresponses by optimizing process settings in experimental design. Initially, mathematical relationships were constructed between each response’s replicate and the controllable process factors. Using Genetic algorithm, the optimal settings of controllable process factors for each response’s replicate were determined and then employed to build a fuzzy regression model of each quality response. Fuzzy desirability and deviation matrices were then developed. Finally, fuzzy optimization models were developed and then solved. Three real applications were provided for illustration. Optimization results revealed that the developed optimization procedure has efficiently optimized process performance to enhance multiresponses under uncertainty. In contrast to the Taguchi method, grey-Taguchi technique, and artificial neural networks approach, the proposed procedure efficiently optimized process performance for multiresponses under uncertainty.
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