Skip to main content

Intelligent scheduling systems: an artificial-intelligence-based approach

  • Chapter
Manufacturing Decision Support Systems

Part of the book series: Manufacturing Systems Engineering Series ((MSES,volume 1))

  • 198 Accesses

Abstract

Current developments in the field of microelectronics and computing technology have made it possible to apply the results achieved in the field of artificial intelligence (AI) to developing systems that exhibit the attributes of AI. There are potentially numerous benefits that could be realized by incorporating the new trends evolving in AI to develop systems that could search for new heuristic sequencing/scheduling rules in complex manufacturing systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Belew R.K. and Booker, L.B. (eds) (1991) Proceedings of the Fourth International Conference on Genetic Algorithms, University of California, San Diego. Morgan Kaufmann Publishers, San Mateo, CA.

    Google Scholar 

  • Booker, L.B., Goldberg, D.E. and Holland, J.H. (1989) Classifier systems and genetic algorithms. Artificial Intelligence, no. 40, 235–282.

    Google Scholar 

  • Coombs, S. and Davis, L. (1987) Genetic algorithms and communication link speed design: constraints and operators, in Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 257–260.

    Google Scholar 

  • Davis, L. and Coombs, S. (1987) Genetic algorithms and communication link speed design: theoretical considerations, in Proceedings of the Second International Conference on Genetic Algorithms, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 252–256.

    Google Scholar 

  • Davis, L. and Coombs, S. (1989) Optimizing network link sizes with genetic algorithms, in Modelling and Simulation Methodology: Knowledge Systems Paradigms (eds M. Elzas, T. Oren and B.P. Zeigler) North Holland, Amsterdam.

    Google Scholar 

  • DeJong, K.A. (1975) Analysis of the behavior of a class of genetic adaptive systems. PhD Dissertation, Department of Computer and Communication Sciences, University of Michigan.

    Google Scholar 

  • Goldberg, D.E. (1983) Computer-aided gas pipeline operation using genetic algorithms and rule learning. PhD Dissertation, College of Engineering, University of Alabama.

    Google Scholar 

  • Goldberg, D.E. (1989) Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, MA.

    MATH  Google Scholar 

  • Goldberg, D.E. and Kuo, C.H. (1987) Genetic algorithms in pipeline optimization. Journal of Computing in Civil Engineering, 1, 128–141.

    Article  Google Scholar 

  • Grefenstette, J.J. and Fitzpatrick, J.M. (1985) Genetic search with approximate function evaluations, in Proceedings of an International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 112–120.

    Google Scholar 

  • Grefenstette, J.J., Gopal, R., Rosmaita, B. and Van Gucht, D. (1985) Genetic algorithms for the travelling salesman problem, in Proceedings of the First International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 160–168.

    Google Scholar 

  • Hilliard, M.R., Liepins, G.E., Rangarajan, G. and Palmer, M. (1989) Learning decision rules for scheduling problems: a classifier hybrid approach, in Proceedings of the Sixth International Conference on Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA, pp. 188–200.

    Google Scholar 

  • Holland, J.H. (1975) Adaptation in Natural and Artificial Systems, The University of Michigan Press, Michigan.

    Google Scholar 

  • Holland, J.H. and Reitman, J.S. (1978) Cognitive systems based on adaptive algorithms, in Pattern Directed Inference Systems (eds D.A. Waterman, and F. Hayes-Roth), Academic Press, New York.

    Google Scholar 

  • Holland, J.H., Holyoak, K.J., Nisbett, R.E. and Thagard, P.R. (1986) Induction: Processes of Inference, Learning, and Discovery, The MIT Press, Cambridge, MA.

    Google Scholar 

  • Kuchinski, M.J. (1985) Battle management systems control rule optimization using artificial intelligence. Technical Report No. NSWC MP 84–329, Naval Surface Weapons Center, Dahlgren, VA.

    Google Scholar 

  • Michalski, R.S. and Kodratoff, Y. (1990) Research in machine learning: recent progress, classification of methods, and future directions, in Machine Learning: An Artificial Intelligence Approach (eds Y. Kodratoff, and R.S Michalski), Vol. 3, Morgan Kaufmann Publishers, San Mateo, CA.

    Google Scholar 

  • Oliver, I., Smith, D. and Holland, J. (1987) A study of permutation crossover operators on the travelling salesman problem, in Proceedings of the Second International Conference on Genetic Algorithms and Their Applications, Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 224–230.

    Google Scholar 

  • Smith, S.F. (1980) A learning system based on genetic adaptive algorithms. PhD Dissertation, Department of Computer Science, University of Pittsburgh.

    Google Scholar 

  • Starkweather, T., McDaniel, S., Mathias, K., Whitley, D. and Whitley, C. (1991) A comparison of genetic sequencing operators, in Proceedings of the Fourth International Conference on Genetic Algorithms (eds R.K. Belew and R.L. Booker), University of California, San Diego. Morgan Kaufmann Publishers, San Mateo, CA, pp. 69–76.

    Google Scholar 

  • Syswerda, G. (1989) Uniform crossover in genetic algorithms, in Proceedings of the Third International Conference on Genetic Algorithms (ed. D. Schaffer), Morgan Kaufmann Publishers, San Mateo, CA, pp. 67–72.

    Google Scholar 

  • Syswerda, G. and Palmucci, J. (1991) The application of genetic algorithms to resource scheduling, in Proceedings of the Fourth International Conference on Genetic Algorithms (eds R.K. Belew and R.L. Booker), University of California, San Diego. Morgan Kaufmann Publishers, San Mateo, CA. pp. 502–508.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Chapman & Hall

About this chapter

Cite this chapter

Zaveri, J.S., Emdad, A.F. (1997). Intelligent scheduling systems: an artificial-intelligence-based approach. In: Parsaei, H.R., Kolli, S., Hanley, T.R. (eds) Manufacturing Decision Support Systems. Manufacturing Systems Engineering Series, vol 1. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1189-8_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-1189-8_10

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8505-2

  • Online ISBN: 978-1-4613-1189-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics