An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

Production Planning and Control

Volumn 13, Issue 8, 2002, Pages 715-724

  Mathieu, Richard G ^a   Wray, Barry A ^b   Markham, Ina S ^c  

^a SAINT LOUIS UNIVERSITY   (United States)

^b UNIVERSITY OF NORTH CAROLINA WILMINGTON   (United States)

^c JAMES MADISON UNIVERSITY   (United States)

Author keywords

Classification and regression trees (CART); Just in time; Kanban; Rule induction

Indexed keywords

COMPUTER INTEGRATED MANUFACTURING; COMPUTER SIMULATION; COSTS; DATA ACQUISITION; DATA REDUCTION; DECISION THEORY; INDUSTRIAL MANAGEMENT; OPERATIONS RESEARCH; PRODUCTIVITY;

CLASSIFICATION AND REGRESSION TREES; INDUCTIVE LEARNING; KANBAN-BASED JUST-IN-TIME PRODUCTION SYSTEM;

JUST IN TIME PRODUCTION;

EID: 0036941873     PISSN: 09537287     EISSN: None     Source Type: Journal    
DOI: 10.1080/0953728031000057343     Document Type: Article

References (24)

1. Breiman, L., Ffiedman, J., Olshen, R., and Stone, C. J., 1984, Classification and Regression Trees (Wadsworth, Belmant, CA).
2. Crawford, S. L., 1989, Extensions to the Cart algorithm. International Journal of Man-Machine Studies, 31, 197-217.
3. Deleersnyder, J. Hodgson, T. Muller, H., and O'Grady, P., 1989, Kanban controlled pull systems: an analytic approach. Management Science, 35(9), 1079-1091.
4. Feigenbaum, E. A., 1981, Expert Systems in the 1980s, State of the Art Report on Machine Intelligence (Pergamon-Infotech, Maidenhead).
5. Fukukawa, T., and Hong, S. C., 1993, The determination of the optimal number of Kanbans in a just-in-time production system. Computers and Industrial Engineering, 24(4), 551-559.
6. Gupta, Y., and Gupta, M., 1989. A system dynamics model for a multi-stage multi-line dual-card JIT Kanban system. International Journal of Production Research, 27(2), 309-352.
7. Hall, D., Hansen, R., and Lang, D., 1997, The negative information problem in mechanical diagnostics. Journal of Engineering for Gas Turbines and Power, 119, 370-377.
8. Holland, J. H., Holyoak, K. J., Nisbett, R. E., and Thagard, P. R., 1986, Induction: Process of Inference, Learning, and Discovery (The MIT Press, Cambridge, MA).
9. Huang, P., Rees, L., and Taylor, B., 1983, A simulation analysis of the Japanese just-in-time technique (with Kanbans) for a multi-line, multi-stage production system. Decision Sciences, 14(3), 326-344.
10. Hunt, E., Marin, J., and Stone, P., 1966, Experiments in Induction (Academic Press, New York).
11. Markham, I., Mathieu, R., and Wray, B., 1998, A rule induction approach for determining the number of Kanbans in a just-in-time production system. Computers and Industrial Engineering, 34, 717-726.
12. Markham, I., Mathieu, R., and Wray, B., 2000, Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees. Integrated Manufacturing Systems: the International Journal of Manufacturing Technology Management, 11(4), 239-246.
13. Minsky, M., 1994, Negative expertise. International Journal of Expert Systems, 7(1), 13-19.
14. Philipoom, P., Rees, B., Taylor, B., and Huang, P., 1987, An investigation of the factors influencing the number of Kanbans required in the implementation of the JIT technique with Kanbans. International Journal of Production Research, 25(3), 457-472.
15. Pice, W., Gravel, M., and Nsakanda, A. L., 1994, A review of optimization models of kanban-based production systems. European Journal of Operational Research, 75, 1-12.
16. Pritsker, A., Sigal, C., and Hammersfahr, R., 1989, SLAM II: Network Models for Decision Support (Prentice-Hall, Englewood Cliffs, NJ).
17. Quinlan, J. R., 1979, Discovering rules by induction from large collections of example, in D. Michie, (Ed). Expert Systems in the Micro Electronic Age (The Edinburgh University Press, Edinburgh), 168-201.
18. Ragatz, G., and Mabert, V., 1984, A simulation analysis of due date assignment rules. Journal of Operations Management, 5(1), 27-39.
19. Rakes, T., Rees, L., Stochi, F., and Wray, B., 1994, Estimating the number of kanbans using neural networks. Advances in Artificial Intelligence in Economics, Finance, and Management, 1, 125-139.
20. Triantaphyllou, E., and Soyster, A., 1996, On the minimum number of logical clauses inferred from examples. Computers & Operations Research, 23(8), 783-800.
21. Vollman, T., Berry, W., and Whybark, D. C., 1991. Manufacturing Planning and Control Systems (Irwin, Chicago).
22. Wray, B. A., 1992a, Prediction and control in a Just-In-Time environment using neural networks. Dissertation, Department of Management Science, Virginia Tech, Blacksburg, Va.
23. Wray, B. A. 1992b, Dynamically adjusting the number of Kanbans in a stochastic JIT environment: an application of neural networks. Proceedings of the Twenty-Fourth Annual Meeting of the Decision Sciences Institute, San Francisco, pp 735-737.
24. Wray, B., Rakes, T., and Rees, L., 1997, Identifying critical factors in a dynamic JIT environment using neural networks. Journal of Intelligent Manufacturing, 8, 83-96.