Manufacturing Executive Systems Optimizations

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1.INTRODUCTION Manufacturing Execution Systems (MES) applications have become essential to support real-time production control as well as data collection and require improving production performance. MES has significantly evolved into more powerful and more integrated software applications as computing technologies. This is due to the capability of MES optimizer business processes in the product supply chain, improve product of quality and ensure the safety of manufacturing processes, which can link to automation equipment to record relevant information and to help manufacturers solve these challenges by tracking each movement in real-time. Jindal et al. [1], process information is captured and validated as it happens, and manufacturers can tracks yield and cycle time by operator, equipment, production line, and adjust manufacture processes to accelerate development. Younus, M et al.[2], Integrated MES in a manufacture industry start, guide, respond to and report on manufacturing activities as they occur from order launch to finished product by improving quality and reducing costs of the product. Products can be tracked throughout the manufacture process using the work-in-process (WIP) tools in MES Factory Server, reducing time to complete the product. Hao Guangke et al [3] Service-oriented MES is well configurable so that it can facilitate system fast practices and workshop business agility. Valckenaers P et al [4] the MES performs this task in an autonomic manner, filling in missing details, providing alternatives for unfeasible assignments, handling auxiliary tasks, and so on. MESA international [5] study of benefits is part of MESA’s aggressive research on the analysis programme designed to support developers/vendors of... ... middle of paper ... ... J. Sheppard and M. Kaufman, A Bayesian Approach to Diagnosis and Prognosis Using Built-In Test, IEEE Transactions on Instrumentation and Measurement, Vol. 54(3)pp:1003-1018 (2005). 15. J. Pearl, Probabilistic Reasoning in Intelligent Systems: networks of plausible inference. Morgan Kaufmann Publishers, Inc., San Francisco, CA. 1988. 16. J.H.Kim,and J.pearl ,A computational model for causal and diagnostic reasoning in Inference engines[C].In Proc.B`h Int. Joint Conf. On Artificial Intelligence, Karlsrube,West Germany, pp:190-193(1983) 17. D. Heckerman , Causal independence for knowledge acquisition and inference. In Proceedings.9th Conference on Uncertainty in Artificial Intelligence, pp:122-127(1993). 18. D. Heckerman, and J. Breese ,A new look at causal independence. In Proc. of the Tenth Conference on Uncertainty in Artificial Ingelligence, pp:286-292(1994).
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