Abstract—A prototype simulation-optimization system called AnyPLOS, which couples an Artificial Neural Network (ANN) based simulation model with a genetic algorithm optimization model, is presented. AnyPLOS is designed to discover value of effective input parameters of a production line so that all required quality control tests on the output product is satisfied. First an ANN was trained and tested to provide an acceptable level of accuracy in prediction of production line outputs, and then it was coupled with a GA optimization module to find desired solutions. A real world case study, in Erish Khodro manufacturing company, was set up and the foam production process input parameters were optimized so that the produced samples satisfied quality requirements. In order to verify the results, discovered solutions were used to produce real foam samples in the production line. After that, quality control tests were performed on samples. Quality test results were, as predicted by ANN, within the desired range. In order to estimate the performance of the trained ANN, experimental observations were compared to values which were predicted by ANN. A convincing correlation was found between ANN predictions and experimental values.
Index Terms—AnyPLOS, genetic algorithm, neural network, cold foam, production line.
M. Jahanpour was with Iran University of Science and Technology, Tehran, Iran (e-mail: ajahanpur@civileng.iust.ac.ir).
K. Farnian and K. Tahouri were with Central Branch of Islamic Azad University, Tehran, Iran. (e-mail: farnian@sabalift.com, kourosht50@gmail.com).
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Cite:Mohammad Amin Jahanpour, Kamran Farnian, and Kourosh Tahouri, "Application of a Coupled Simulation-Optimization System Called AnyPLOS in a Cold Foam Production Line," International Journal of Modeling and Optimization vol. 3, no. 2, pp. 221-225, 2013.
