An estimation of production system design parameters with a new network approach: A case study in the ceramic firm

A new artificial network (ANN) approach has been developed in this study. The problem is the determine production system parameters with considering as an example such as, toilet marble, washbasin and clset. The following stages are used in order to solve the production system parameters. At the first stage, the production system of three products in modeled. At the second stage, the system is simulated and generated system performance values under different system parameters. At the third stage, an ANN is developed and trained with using the simulation results. At the last stage, the trained neural network estimates the system parameters with considering production quantities of the products. During teaching process of the neural network, the system parameters as input variables and production quantities as output variables are considered. The results show that the new proposed ANN estimates much faster and finds out more accurate variables than a simulation approach in terms of determining system parameters with considering production demands of the products. The approach has been tested for the company and results were found very encouraging.