Performance analysis of an industrial system using soft computing based hybridized technique
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Due to imprecise information, it is always difficult for the system analyst to predict and enhance the performance of the system up to the desired degree of accuracy. Therefore, the main task is to reduce the uncertainty level for decision makers, so as to take a more sound decision in a reasonable time. For handling these issues, this paper addressed the various reliability parameters of the industrial system, which depicts the behavior of the system, by quantifying the uncertainties in the data in the form of fuzzy numbers. The corresponding membership functions of the system’s parameters are computed by formulating a nonlinear optimization model and solve it. The obtained results were compared with the existing as well as traditional methodology and results and found that they had less range of uncertainties during the analysis. A sensitivity as well as performance analysis has also been done for depicting the most critical component of the system. Finally, an approach has been illustrated through a case study of cattle feed plant, a repairable industrial system.
KeywordsUncertain data PSOBLT Fuzzy membership function Nonlinear optimization.
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