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Selected Topics in Artificial Intelligence for Planning and Scheduling Problems, Knowledge Acquisition, and Machine Learning

  • Conference paper
Batch Processing Systems Engineering

Part of the book series: NATO ASI Series ((NATO ASI F,volume 143))

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Abstract

Application of artificial intelligence techniques to planning and scheduling problems are briefly reviewed, issues involved in knowledge acquisition are discussed, and methods for machine learning are introduced.

More in depth treatise of selected approaches, with processing systems engineering examples, are used to illustrate some of the issues involved. The examples utilize genetic programming for batch scheduling and design problems, and a hybrid of symbolic and connectionist approaches to automated knowledge acquisition (machine learning).

Concepts and issues involved in Genetic Programming are discussed within the context of batch processing systems examples. A scheduling example is used to illustrate discrete variable based decision models and associated terminology while a design problem with continuous decision variables and constraints is also optimized. Possibility of using genetic programming as an integrating tool for computer integrated manufacturing is also discussed.

A novel instance based learning algorithm that allows symbolic information to be encoded into a connectionist representation is introduced. Selection of complex distillation column sequencing designs is used as the example. However, the approach is ideally suited for fault diagnosis and structured selection problems. The knowledge/rules extracted from the learning algorithm are very similar to the design heuristics proposed in literature. The performance of the Symbolic Connectionist network (SC-net) is studied based on its knowledge extraction capabilities and the classification accuracy in the test case.

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Author information

Authors and Affiliations

  1. College of Engineering, University of South Florida Tampa, FL, 33620, USA

    Aydin K. Sunol, Muzaffer Kapanoglu & Praveen K. Mogili

Authors
  1. Aydin K. Sunol
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  2. Muzaffer Kapanoglu
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  3. Praveen K. Mogili
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Editor information

Editors and Affiliations

  1. School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Ginraras V. Reklaitis

  2. Department of Chemical Engineering, College of Engineering University of South Florida, 4202 East Fowler Avenue, ENG 118, Tampa, FL, 33620-5350, USA

    Aydin K. Sunol

  3. Laboratory for Technical Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, Switzerland

    David W. T. Rippin

  4. Department of Chemical Engineering, Boğaziçi University, TR-80815, Bebek-Istanbul, Turkey

    Öner Hortaçsu

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© 1996 Springer-Verlag Berlin Heidelberg

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Sunol, A.K., Kapanoglu, M., Mogili, P.K. (1996). Selected Topics in Artificial Intelligence for Planning and Scheduling Problems, Knowledge Acquisition, and Machine Learning. In: Reklaitis, G.V., Sunol, A.K., Rippin, D.W.T., Hortaçsu, Ö. (eds) Batch Processing Systems Engineering. NATO ASI Series, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60972-5_25

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  • DOI: https://doi.org/10.1007/978-3-642-60972-5_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64635-5

  • Online ISBN: 978-3-642-60972-5

  • eBook Packages: Springer Book Archive

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