Abstract
Simulation is a popular methodology for analyzing complex manufacturing environments. According to the large number of output of simulations, interpreting them seems impossible. In this paper we use an innovative methodology that combines simulation and data mining techniques to discover knowledge that can be derived from results of simulations. Data used in simulation process, are independent and identically distributed with a normal distribution, but the output data from simulations are often not i.i.d. normal. Therefore by finding associations between output data mining techniques can operate well. Analyzers change the sequences and values of input data according to the importance they have. These operations optimize the simulation output analysis. The methods presented here will of most interest to those analysts wishing to extract much information from their simulation models. The proposed approach has been implemented and run on a supply chain system simulation. The results show optimizations on analysis of simulation output of the mentioned system. Simulation results show high improvement in proposed approach.
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Ghasemi, S., Ghasemi, M., Ghasemi, M. (2011). Knowledge Discovery in Discrete Event Simulation Output Analysis. In: Pichappan, P., Ahmadi, H., Ariwa, E. (eds) Innovative Computing Technology. INCT 2011. Communications in Computer and Information Science, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27337-7_11
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DOI: https://doi.org/10.1007/978-3-642-27337-7_11
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