Abstract
With the growing uncertainty and complexity in the manufacturing environment, most scheduling problems have been proven to be NP-complete and this can degrade the performance of conventional operations research (OR) techniques. This article presents a system-attribute-oriented knowledge-based scheduling system (SAOSS) with inductive learning capability. With the rich heritage from artificial intelligence (AI), SAOSS takes a multialgorithm paradigm which makes it more intelligent, flexible, and suitable than others for tackling complicated, dynamic scheduling problems.
SAOSS employs an efficient and effective inductive learning method, a continuous iterative dichotomister 3 (CID3) algorithm, to induce decision rules for scheduling by converting corresponding decision trees into hidden layers of a self-generated neural network. Connection weights between hidden units imply the scheduling heuristics, which are then formulated into scheduling rules. An FMS scheduling problem is also given for illustration. The scheduling results show that the system-attribute-oriented knowledge-based approach is capable of addressing dynamic scheduling problems.
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References
Chandra, J. and Talavage, J., “Intelligent Dispatching for Flexible Manufacturing,”International Journal of Production Research, Vol. 29, No. 11, pp. 2259–2278 (1991).
Chang, Y.L. and Sullivan, R.S., “Scheduling Generation in a Dynamic Job Shop,”International Journal of Production Research, Vol. 28, No. 1, pp. 65–74 (1990).
Chen, H., “An Intelligent Knowledge Based Scheduling Systems with Inductive Learning Capability Using Neural Networks,” Master Thesis, National Tsing Hua University, Taiwan, R.O.C. (1993).
Dayhoff, J.E.,Neural Network Architecture, Van Nostrand Reinhold, New York, NY (1990).
French, S.,Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop, John Wiley and Sons, Inc., New York, NY (1982).
Graves, S.C., “A Review of Production Scheduling,”Operations Research, Vol. 29, No. 4, pp. 647–675 (1981).
Kusiak, A.,Intelligent Manufacturing Systems, Prentice-Hall, Inc., Englewood Cliffs, NJ (1990).
Kusiak, A. and Ahn, J., “Intelligent Scheduling of Automated Machining Systems,”Computer-Integrated Manufacturing Systems, Vol. 5, No. 1, pp. 3–14 (February 1992).
Leisten, R., “Flowshop Sequencing Problems With Limited Buffer Storage,”International Journal of Production Research, Vol. 28, No. 11, pp. 2085–2100 (1990).
Liu, N. and Cios, K.J., “A Machine Learning Method for Generation of a Neural Network Architecture: A Continuous ID3 Algorithm,”IEEE Transactions on Neural Networks, Vol. 3, No. 2, pp. 280–291 (March 1992).
Nakasuka, S. and Yoshida, T., “Dynamic Scheduling System Utilizing Machine Learning as a Knowledge Acquisition Tool,”International Journal of Production Research, Vol. 30, No. 2, pp. 411–431 (1992).
Nilsson, N.J.,The Mathematical Foundations of Learning Machines, Los Altos, CA (1990).
Quinlan, J.R., “Learning Efficient Classification Procedures and Their Application to the Chess End-Games,” inMachine Learning, R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Eds.), Tioga Publ., Los Altos, CA, pp. 463–481, (1983).
Rachamadugu, R. and Stecke, K.E., “Classification and Review of FMS Scheduling Procedures,”Production Planning and Control, Vol. 5, No. 1, (1994).
Ramasesh, R., “Dynamic Job Shop Scheduling: A Survey of Simulation Research,”International Journal of Management Science, Vol. 18, No. 1, pp. 43–57 (1990).
Shaw, M.J., Park, S., and Raman, N., “Intelligent Scheduling with Machine Learning Capability: The Induction of Scheduling Knowledge,”IIE Transactions, Vol. 24, No. 2, pp. 156–167 (May 1992).
Shaw, M.J. and Whinston, A.B., “An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems,”IIE Transactions, Vol. 21, No. 2, pp. 170–183 (June 1989).
Wein, L.M. and Ou, J., “The Impact of Processing Time Knowledge on Dynamic Job-Shop Scheduling,”Journal of Operational Research Society, Vol. 42, No. 1, pp. 75–82 (1991).
Widrow, B., Winter, R.G., and Baxter, R.A., “Layered Neural Nets for Pattern Recognition,”IEEE Transactions on Acoustics, Speech, Signal Processing, Vol. 36, No. 7, pp. 1109–1118 (1988).
Yih, Y., “Learning Real-time Scheduling Rules from Optimal Policy of Semi-Markov Decision Processes,”International Journal of Computer Integrated Manufacturing, Vol. 5, No. 3, pp. 171–181 (1992).
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Wang, LC., Chen, HM. & Liu, CM. Intelligent scheduling of FMSs with inductive learning capability using neural networks. Int J Flex Manuf Syst 7, 147–175 (1995). https://doi.org/10.1007/BF01358907
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DOI: https://doi.org/10.1007/BF01358907