Application of Neural Networks and Simulation Modeling in Manufacturing System Design

Mansooreh Mollaghasemi, Kenneth LeCroy and Michael Georgiopoulos
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Mansooreh Mollaghasemi: Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, Florida 32816
Kenneth LeCroy: Lucent Technologies, 9333 S. John Young Parkway, Orlando, Florida 32819
Michael Georgiopoulos: Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida 32816

Interfaces, 1998, vol. 28, issue 5, 100-114

Abstract: 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.

Keywords: simulation; application; industries; computer/electronic (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:28:y:1998:i:5:p:100-114

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