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Application of Neural Networks and Simulation Modeling in Manufacturing System Design

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mansooreh Mollaghasemi & Kenneth LeCroy & Michael Georgiopoulos, 1998. "Application of Neural Networks and Simulation Modeling in Manufacturing System Design," Interfaces, INFORMS, vol. 28(5), pages 100-114, October.
    • Handle: RePEc:inm:orinte:v:28:y:1998:i:5:p:100-114
    DOI: 10.1287/inte.28.5.100
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    Cited by:

    1. Kumar Rajaram & Charles J. Corbett, 2002. "Achieving Environmental and Productivity Improvements Through Model-Based Process Redesign," Operations Research, INFORMS, vol. 50(5), pages 751-763, October.
    2. B Dengiz & C Alabas-Uslu & O Dengiz, 2009. "Optimization of manufacturing systems using a neural network metamodel with a new training approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(9), pages 1191-1197, September.

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