Abstract
We study the problem of scheduling a Flexible Flow System (FFS) while utilizing machine learning techniques. Specifically, the effect of incorporating Feature Construction in an existing adaptive learning-based decision support system is investigated. The primary purpose of constructing features is to increase the efficiency of obtaining information from available data by using new features in addition to the initial set of features. There is a need to obtain information effectively while scheduling an FFS. The incorporation of feature construction in such a framework is thus beneficial, which is documented in our preliminary results.
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References
A. Agnetis, C. Arbib and K.E. Stecke, Optimal two-machine scheduling in a flexible flow system, in: Proceedings of the 2nd International Conference on Computer Integrated Manufacturing 1990, pp. 47- 54.
H. Aytug, S. Bhattacharyya, G.J. Koehler and J.L. Snowdon, A review of machine learning in scheduling, IEEE Transactions on Engineering Management 41(1994)165 - 171.
K.R. Baker, Introduction to Sequencing and Scheduling Wiley, New York, 1974.
K.R. Baker, Sequencing rules and due date assignments in a job shop, Management Science 30 (1984)1093 - 1104.
J.P. Callan and P.E. Utgoff, A transformational approach to constructive induction, in: Machine Learning: Proceedings of the 8th International Workshop 1991, pp. 122 - 126.
D.G. Dannenbring, An evaluation of flow shop sequencing heuristics, Management Science 23 (1977)1174 - 1182.
Y.P. Gupta and S. Goyal, Flexibility of manufacturing systems: Concepts and measurements, European Journal of Operational Research 43(1989)119 - 135.
K.L. Hitz, Scheduling of flexible flowshops - I, Technical Report LIDS-R-879, Laboratory for Information and Decision Systems, MIT, 1979.
P. Langley, J.M. Zytkow, H.A. Simon and G.L. Bradshaw, The search for regularity: Four aspects of scientific discovery, in: Machine Learning: An Artificial Intelligence Approach Vol. II, Morgan Kaufmann, Los Altos, 1986, pp. 425- 470.
C.J. Matheus and L. Rendell, Constructive induction in decision trees, Proceedings of the 11th IJCAI 1989, pp. 645 - 650.
S. McClelland, Advancing Surface Mount Technology: An IFS Executive Briefing Springer, 1988.
T.E. Morton and D. W. Pentico, Heuristic Scheduling Systems Wiley-Interscience, 1993.
S. Nakasuka and T. Yoshida, Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool, International Journal of Production Research 30(1992)411- 431.
R.M. O'Keefe and R. Rao, Part input into a flexible flow system: An evaluation of look-ahead simulation and a fuzzy rule base, Technical Report, Dept. of DSES, Rensselear Polytechnic Institute, 1990.
P.S. Ow, and S.F. Smith, Viewing scheduling as an opportunistic problem-solving process, Annals of Operations Research 12(1988) 85 - 108.
G. Pagallo, Learning DNF by decision trees, Proceedings of the 11th IJCAI 1989, pp. 639 - 644.
S. Piramuthu and H. Ragavan, Improving connectionist learning with symbolic feature construction, Connection Science 1(1992)33 - 43.
S. Piramuthu, N. Raman and M.J. Shaw, Learning-based scheduling in a flexible manufacturing flow line, IEEE Transactions on Engineering Management 41(1994)172 - 182.
P. Politakis and S.M. Weiss, Using empirical analysis to refine expert system knowledge bases, Artificial Intelligence 22(1984)23- 48.
J.R. Quinlan, Induction of decision trees, Machine Learning 1(1986)81 - 106
J.R. Quinlan, Decision trees and decision making, IEEE Transactions on Systems, Man, and Cybernetics 20(1990)339 - 346.
H. Ragavan and L. Rendell, Relieving limitations of feature construction algorithms, Proceedings of the Change of Representations Workshop 12th International Joint Conference on Artificial Intelligence 1991, pp. 27 - 36.
L. Rendell and R. Seshu, Learning hard concepts through constructive induction: Framework and rationale, Computational Intelligence 6(1990)247 - 270.
F.A. Rodammer and K.P. White, A recent survey of production scheduling, IEEE Transactions on Systems, Man and Cybernetics 18(1988)6.
J.C. Schlimmer and D. Fisher, A case study of incremental concept induction, Proceedings of the 5th National Conference on Artificial Intelligence 1986, pp. 496 - 501.
M.J. Shaw, Inductive learning for enhancing knowledge-based expert systems, Decision Support Systems 3(1987)319 - 332.
M.J. Shaw, S.C. Park and N. Raman, Intelligent scheduling with machine learning capabilities: The induction of scheduling knowledge, IIE Transactions 24(1992)156 - 168.
C. Sriskandarajah and S.P. Sethi, Scheduling algorithms for flexible flowshops: Worst and average case performance, European Journal of Operational Research 43(1989)143-160.
S. Watanabe, Pattern Recognition: Human and MechanicalWiley, New York, 1985.
R.J. Wittrock, An adaptive scheduling algorithm for flexible flow lines, Operations Research 36 (1988)445- 453.
D-S. Yang, L. Rendell and G. Blix, A scheme for feature construction and a comparison of empirical methods, Proceedings of the 12th IJCAI Sydney, 1991, pp. 699 - 704.
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Piramuthu, S., Raman, N. & Shaw, M.J. Decision support system for scheduling a Flexible Flow System: Incorporation of feature construction. Annals of Operations Research 78, 219–234 (1998). https://doi.org/10.1023/A:1018902200919
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DOI: https://doi.org/10.1023/A:1018902200919