Abstract
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.
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References
Barnes, J.W. and Laguna, M. A. Tabu Search Experience in Production Scheduling. Annals of Operations Research 41, pp. 141–156, 1993.
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.
Burke, P. and Prosser, P. A Distributed Asynchronous System for Predictive and Reactive Scheduling, Artificial Intelligence in Engineering 6, pp. 106–124, 1991.
Collinot, A., LePape, C., and Pinoteau, G. SONIA: A Knowledge-based Scheduling System, Artificial Intelligence in Engineering 2 (2) pp. 86–94, 1988.
Dagostar, A. S. A Decentralized Reactive Fuzzy Scheduling system for Cellular Manufacturing systems, Ph. D. Thesis Univ. of New South Wales, Australia, 1996.
Dorn, J. Supporting Scheduling with Temporal Logic, Proceedings of the IJCAI’93 Workshop on Production Planning, Scheduling and Control, Chambéry, France, 1993.
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.
Dorn, J. Iterative Improvement Methods for Knowledge-based Scheduling, AICOM Journal, pp. 20–34, March, 1995.
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.
Dorn, J. and Shams, R. Scheduling High-grade Steel Making, IEEE Expert, February, pp. 28–35, 1996.
Dorn, J., Girsch, M., Skele, G. and Slany, W. Comparison of Iterative Improvement Techniques for Schedule Optimization, European Journal on Operational Research 94, pp. 349–361, 1996.
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.
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.
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.
Fox, M. S. Constraint-Directed Search: A Case Study of Job-Shop Scheduling, London: Pitman, 1987.
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.
Fox, M. S. ISIS: Retrospective, in Zweben and Fox (eds) Intelligent Scheduling, Morgan Kaufmann, pp. 3–28, 1994.
French, S. Sequencing and Scheduling — An Introduction to the Mathematics of the Job-Shop. Chichester: Ellis Horwood, 1982.
Glaser, J. Tabu-Suche für Reaktives Scheduling anhand eines Beispiels aus der Stahlindustrie, Diplomarbeit Technische Universität Wien, 1996.
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.
Kaufmann, A. and Gupta, M.M. Intoduction to Fuzzy Arithmetic: Theory and Applications. New York: van Nostrand Reinhold, 1985.
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.
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.
Klaue, R. Simulation für Reaktives Scheduling anhand eines Beispiels aus der Stahlindustrie, Diplomarbeit Technische Universität Wien, 1996.
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.
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.
Sadeh, N.Look-ahead Techniques for Micro-opportunistic Job Shop Scheduling Ph. D. Thesis School of Computer Science, Carnegie Mellon University, Pittsburgh, 1991.
Slany, W. Scheduling as a Fuzzy Multiple Criteria Optimization Problem, Ph. D. Thesis Technische Universität Wien, 1994.
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.
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.
Zweben, M., Davis, E., Daun, B. and Deale, M.J. Scheduling and Rescheduling with Iterative Repair, IEEE Transactions on Systems, Man, and Cybernetics 23 (6), pp. 1588–1596, 1992.
Zadeh, L. Fuzzy Sets, Information and Control 8 p 338, 1965.
Zimmermann, H.-J., Zadeh, L. A.and Gaines, B. R. Fuzzy sets and decision analysis, Amsterdam: North Holland, 1984.
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Dorn, J., Kerr, R., Thalhammer, G. (1998). Maintaining Robust Schedules by Fuzzy Reasoning. In: Drexl, A., Kimms, A. (eds) Beyond Manufacturing Resource Planning (MRP II). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03742-3_11
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DOI: https://doi.org/10.1007/978-3-662-03742-3_11
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