Application of neural networks and simulation modeling in manufacturing system design

Simulation modeling is often used in the design of manufacturing systems. With simulation modeling, however, the design process is a trial-and-error process; that is, an estimated ‘good’ design is input to the model. Based upon the ‘quality’ of this design, the designer may input a slightly perturbed design. This iterative process continues until the designer is ‘satisfied’. This process can be very time consuming. Neural networks can be used in conjunction with simulation modeling for system design to eliminate the trial-and-error process. This approach is used to achieve the opposite of what a simulation model can achieve. That is, given a set of desired performance measures, the neural network outputs a suitable design to meet management goals. In a real-world application, a major semiconductor manufacturing plant used this methodology to determine how the test operation should be operated to achieve the production goals.