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Hybrid intelligent systems: Tools for decision making in intelligent manufacturing

  • Chapter
Artificial Neural Networks for Intelligent Manufacturing

Part of the book series: Intelligent Manufacturing Series ((IMS))

Overview

Many intelligent computing techniques have been developed over the last decade. Some of these include neural networks, fuzzy systems, genetic algorithms, rule-based expert systems, inductive expert systems, and the wide array techniques lumped under the heading of artificial intelligence. The application of these intelligent computing techniques to support planning and control in manufacturing can be done at three levels: organization, coordination, and execution. Because of the different nature of the problems at each level, different types of solutions may be required. Until recently, problem solvers have typically used single-technique-based tools to build these solutions, e.g., an expert system based solution, a neural network based solution, or a linear programming solution. Simple problems that fit the assumptions of a single-technique-based solution may be easily solved by such an approach. However, most real world manufacturing problems are not simple. They may neither fit the assumptions of a single technique nor be effectively solved by the strengths and capabilities of a single technique. One approach to deal with these complex real world problems is to integrate the use of two or more techniques in order to combine their different strengths and overcome each other’s weaknesses to generate hybrid solutions.

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Author information

Authors and Affiliations

  1. Graduate School of Management, Kent State University, Kent, Ohio, 44242, USA

    Gregory R. Madey, Jay Weinroth & Vijay Shah

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  1. Gregory R. Madey
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  2. Jay Weinroth
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  3. Vijay Shah
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Editors and Affiliations

  1. Department of Engineering Management, University of Missouri-Rolla, USA

    Cihan H. Dagli (Associate Professor) (Associate Professor)

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© 1994 Springer Science+Business Media Dordrecht

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Madey, G.R., Weinroth, J., Shah, V. (1994). Hybrid intelligent systems: Tools for decision making in intelligent manufacturing. In: Dagli, C.H. (eds) Artificial Neural Networks for Intelligent Manufacturing. Intelligent Manufacturing Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0713-6_4

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  • DOI: https://doi.org/10.1007/978-94-011-0713-6_4

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4307-6

  • Online ISBN: 978-94-011-0713-6

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