ILISCE: A system for learning control heuristics in a scheduling environment

  • Thierry Van de Merckt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 604)


This paper presents a general learning structure called Ilisce which learns meta-rules to guide a scheduling system. The scheduling program, called Opal, solves real-world problems using a set of local heuristics which incrementaly resolve the whole set of conflicts among the physical resources of the floor. A Selective Inductive learning approach is used to create concepts representing typical states of the job shop which allow a Classifier to “recognize” current situations and to choose the corresponding best operators to apply on. In this paper, we present the general architecture of Ilisce.


Scheduling Learning Credit Assignment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Eric Bensana: Utilisation de techniques d'intelligence artificielle pour l'ordonnancement d'ateliers. Thèse de l'Ecole Nationale Supérieure de l'Aéronautique et de l'Espace. Département Automatique,1987.Google Scholar
  2. [2]
    John H. Blackstone, Don T. Phillips, Gary L. Hogg: A State-of-the-Art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research vol 20 n∘ 1, 1982.Google Scholar
  3. [3]
    W.I. Bullers, S.Y. Nof, A.B.Whinston: Artificial Intelligence in Manufacturing Planning and Control. AIIE Transactions, Dec. 1980.Google Scholar
  4. [4]
    Anne Collinot, Claude Le Pape: Adapting the behavior of a job-shop scheduling system. Decision Support Systems 7, North Holland, 1991.Google Scholar
  5. [5]
    Ali S. Kiran, Milton L. Smith: Simulation studies in Job-shop Scheduling — I: A Survey. Computer & Industrial Engineering Vol 8 n∘ 2, 1984.Google Scholar
  6. [6]
    S.J. Noronha, V.V.S. Sarma: Knowledge-Based Approaches for Scheduling Problems: A Survey. IEEE Transactions on Knowledge and Data Engineering, vol. 3 n∘2, 1991.Google Scholar
  7. [7]
    J.Ross Quinlan: The Effect of Noise on Concept Learning. Machine Learning, An Artificial Intelligence Approach. Vol II. Ed. Ryszard S. Michalski, Jaime G. Carbonell & Tom M. Mitchell, Springer Verlag, 1986.Google Scholar
  8. [8]
    R.S. Sutton: Learning to Predict by the method of Temporal Differences. Machine Learning, vol. 3, 1988.Google Scholar
  9. [9]
    T. Van de Merckt: NFDT: A Sytem that Learns Flexible Concepts based on Decision Trees for Numerical Attributes. Proceedings of the Ninth International Machine Learning Conference. Morgan Kaufmann, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Thierry Van de Merckt
    • 1
  1. 1.IRIDIA, Université Libre de BruxellesBruxellesBelgium

Personalised recommendations