Rank order clustering and imperialist competitive optimization based cost and RAM analysis on different industrial sectors

The competitive global scenario faces the corresponding impacts such as difficulties in scheduling and loading, high tooling and equipment investment, enormous scrap availability, complex to control the quality, and extensive setup time. So, a higher level of connectivity is required in between the design and manufacturing activities to raise the profitability of the firms and enrich the product support design. In any manufacturing firm, poor reliability causes failure availability in all stages, namely, design, construction, planning, and maintenance, etc. In this research work, RAM is analyzed from the Weibull distribution based Mean time to repair (MTTR) and mean time to failure rates (MTTF) for the ten different industries. The measured RAM performances are optimized by the Imperialist competitive algorithm (ICA) by the application of the rank order clustering (ROC) method. The failure rates are clustered and ranked by using the rank order clustering method. Besides, the cost and RAM performance values are predicted by ICA and hybrid ROC method, and such a proposed algorithm is mathematically modeled in the Mat Lab platform. Hence from the evaluation, the proposed method scores better industrial performances than the actual and other implemented methods