Maintaining Robust Schedules by Fuzzy Reasoning
Practical scheduling usually has to react to many unpredictable events and uncertainties in the production environment. Although often possible in theory, it is undesirable to reschedule from scratch in such cases. Since the supplier of raw materials and clients will be prepared for the predicted schedule it is important to change only those features of the schedule that are necessary.
We show how on one side fuzzy logic can be used to support the construction of schedules that are robust with respect to changes due to certain types of events. On the other side we show how a reaction can be restricted to a small environment by means of fuzzy constraints and a repair-based problem-solving strategy.
We demonstrate the proposed representation and problem-solving method by introducing a scheduling application in a steelmaking plant. We construct a preliminary schedule by taking into account only the most likely duration of operations. This schedule is iteratively “repaired” until some threshold evaluation is found. A repair is found with a local search procedure based on tabu search. Finally, we show which events can lead to reactive scheduling and how this is supported by the repair strategy.
KeywordsTabu Search Continuous Caster Shop Floor Reactive Schedule Predictive Schedule
Unable to display preview. Download preview PDF.
- Bel, G., Bensana, E., Dubois, D., Erschler, J. and Esquirol, P. A Knowledge Based Approach to Industrial Job Shop Scheduling, in A. Kusiak (ed.) Knowledge Based Systems in Manufacturing, Taylor and Francis, pp. 207–246, 1989.Google Scholar
- Dagostar, A. S. A Decentralized Reactive Fuzzy Scheduling system for Cellular Manufacturing systems, Ph. D. Thesis Univ. of New South Wales, Australia, 1996.Google Scholar
- Dorn, J. Supporting Scheduling with Temporal Logic, Proceedings of the IJCAI’93 Workshop on Production Planning, Scheduling and Control, Chambéry, France, 1993.Google Scholar
- Dorn, J. and Slany, W. A Flow Shop with Compatibility Constraints in a Steel making Plant, in M. Zweben and M. Fox (eds.) Intelligent Scheduling, Morgan Kaufmann, pp. 629–654, 1994.Google Scholar
- Dorn, J. Iterative Improvement Methods for Knowledge-based Scheduling, AICOM Journal, pp. 20–34, March, 1995.Google Scholar
- Dorn, J. Case-based reactive scheduling in Roger Kerr and Elisabeth Szelke (Eds.) Artificial Intelligence in Reactive Scheduling, London: Chapman & Hall, pp. 32–50, 1995.Google Scholar
- Dorn, J. and Shams, R. Scheduling High-grade Steel Making, IEEE Expert, February, pp. 28–35, 1996.Google Scholar
- Dorn, J., Girsch, M. and Vidakis, N. DÉJÀ Vu — A Reusable Framework for the Construction of Intelligent Interactive Schedulers, Advances in Production Management Systems - Perspectives and Future Challenges -, Okino et al. (eds.) Chapman & Hall, 1998.Google Scholar
- Drummond, M., Swanson, K. and Bresina, J. Robust Scheduling and Execution for Automatic Telescopes in Intelligent Scheduling Zweben and Fox (eds.) Morgan Kaufmann, pp. 629–654, 1994.Google Scholar
- Dubois, D. Fargier, H. and Prade H. The use of fuzzy constraints in job-shop scheduling.Proceedings of the IJCAI’93 Workshop on Knowledge-based Production Planning Schedulin and Control. 1993. Google Scholar
- Fox, M. S. Constraint-Directed Search: A Case Study of Job-Shop Scheduling, London: Pitman, 1987.Google Scholar
- Fox, B.R. and McMahon, M.B. Genetic Operators for Sequencing Problem, in G.J.E. Rawlings (ed.) Foundations of Genetic Algorithms, pp. 284–300, 1991.Google Scholar
- Fox, M. S. ISIS: Retrospective, in Zweben and Fox (eds) Intelligent Scheduling, Morgan Kaufmann, pp. 3–28, 1994.Google Scholar
- French, S. Sequencing and Scheduling — An Introduction to the Mathematics of the Job-Shop. Chichester: Ellis Horwood, 1982.Google Scholar
- Glaser, J. Tabu-Suche für Reaktives Scheduling anhand eines Beispiels aus der Stahlindustrie, Diplomarbeit Technische Universität Wien, 1996.Google Scholar
- Hadavi, K. C. ReDS: A Real Time Production Scheduling System from Conception to Practice, in Zweben and Fox (eds) Intelligent Scheduling, Morgan Kaufmann, pp. 581–604, 1994.Google Scholar
- Kaufmann, A. and Gupta, M.M. Intoduction to Fuzzy Arithmetic: Theory and Applications. New York: van Nostrand Reinhold, 1985.Google Scholar
- Keng, N. P., and Yun, D. Y. Y. A Planning/Scheduling Methodology for the Constrained Resource Problem, Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 998–1003, AAA’ Press, 1989.Google Scholar
- Kerr, R.M. and Walker, R. N. A Job Shop Scheduling System Based on Fuzzy Arith metic. Proceedings of the 2nd International Conference on Expert Systems and Leading Edge in Production and Operations Management, Hilton Head Island, S.C., pp. 433–450, 1989.Google Scholar
- Klaue, R. Simulation für Reaktives Scheduling anhand eines Beispiels aus der Stahlindustrie, Diplomarbeit Technische Universität Wien, 1996.Google Scholar
- Le Pape, C. Experiments with a distributed architecture for predictive scheduling and execution monitoring, in Artificial Intelligence in Reactive Scheduling, Kerr and Szelke (Eds.), Chapman & Hall, pp. 129–145, 1995.Google Scholar
- Minton, S., Philipps, A., Johnston, M. and Laird, P. Solving Large Scale CSP and Scheduling Problems with a Heuristic Repair Method. Proceedings of the 8th National Conference on Artificial Intelligence (AAAI’90), pp. 17–24, 1990.Google Scholar
- Sadeh, N.Look-ahead Techniques for Micro-opportunistic Job Shop Scheduling Ph. D. Thesis School of Computer Science, Carnegie Mellon University, Pittsburgh, 1991.Google Scholar
- Slany, W. Scheduling as a Fuzzy Multiple Criteria Optimization Problem, Ph. D. Thesis Technische Universität Wien, 1994.Google Scholar
- Smith S. F. OPIS: A Methodology and Architecture for Reactive Scheduling, in M. Zweben and M. Fox (eds) Intelligent Scheduling, Morgan Kaufmann, pp. 29–66, 1994.Google Scholar
- Tam, M. et al. A Predictive and Reactive Scheduling Tool Kit for Repetitive Manufacturing, in Knowledge-based Reactive Scheduling, E. Szelke and R.M. Kerr (Eds.), Elsevier Science, pp. 147–162, 1994.Google Scholar
- Zimmermann, H.-J., Zadeh, L. A.and Gaines, B. R. Fuzzy sets and decision analysis, Amsterdam: North Holland, 1984.Google Scholar