Abstract
Finite capacity scheduling software packages provide a detailed advance plan of production events. However, the execution of this advance plan is disrupted by a myriad of unanticipated interruptions, such as machine breakdowns, yield variations, and hot jobs. The alternatives available to respond to such interruptions include modifying the existing schedule, regenerating the complete schedule, or doing nothing and letting the production system gradually absorb the impact of the interruption. This article reports on a simulation study aimed at understanding the impact of an interruption on a schedule in order to build a knowledge base for intelligent selection of a response from a set of alternatives. The results of the experimental study are used to identify significant major factors and their interactions. The results are discussed to draw insights into the performance of a flexible manufacturing system following an interruption. The causes leading to particular performance anomalies are extensively discussed and mechanisms for propagation and absorption of the effect of interruptions in manufacturing systems are inferred. Practical implications for the development and implementation of schedules are deduced and areas for further research proposed. This study provides the groundwork necessary to proceed with the development of strategies for responding to interruptions.
Similar content being viewed by others
References
Bean, J. C., Birge, J. R., Mittenthal, J., and Noon, C. E., “Match-up Scheduling with Multiple Resources, Release Dates and Disruptions,” Operations Research, Vol. 39, No.3, pp. 470–483 (May–June 1991).
Chang,Y. L., Sullivan, R. S., and Bagchi, U., “Experimental Investigation of Quasi Realtime Scheduling in Flexible Manufacturing Systems,” Proceedings of the First ORSA/TIMS Conference on Flexible Manufacturing Systems, K. E. Stecke and Rajan Suri (Eds.), Ann Arbor, MI, pp. 307–312 (August 1984).
Filip, F. G., Neagu, G., and Donciulescu, D. A., “Job Shop Scheduling Optimization in Real-time Production Control,” Computers in Industry, Vol. 4, No.4, pp. 395–403 (October 1983).
Foley, W. J., Jain, S., and Haddock, J., “Using Simulation Generators for Modeling Manufacturing Systems,” Progress in Simulation, J. V. Leonard and G. W. Zobrist (Eds.), Ablex, Norwood, NJ (1990).
Goldratt, E. M., “Computerized Shop Floor Scheduling,” International Journal of Production Research, Vol. 26, No.3, pp. 443–455 (March 1988).
Hadavi, K., Shahraray, M. S., and Voigt, K., “ReDS—A Dynamic Planning, Scheduling, and Control System for Manufacturing,” Journal of Manufacturing Systems, Vol. 9, No.4, pp. 332–344 (1990).
Ip, W. H., “Rule-Based ARIMA Models for FMS,” Journal of Materials Processing Technology, Vol. 66, Nos.1–3, pp. 240–243 (April 1997).
Jain, S., “Decision Framework for Interruption Handling in Flexible Manufacturing Systems.” Ph.D. dissertation, Rensselaer Polytechnic Institute, Troy, New York (1988).
Manivannan, S. and Banks, J., “Design of a Knowledge-Based On-Line Simulation System to Control a Manufacturing Shop Floor,” IIE Transactions, Vol. 24, No.3, pp. 72–83 (July 1992).
Muhlemann, A. P., Lockett, A. G., and Farn, C. K., “Job Shop Scheduling Heuristics and Frequency of Scheduling,” International Journal of Production Research, Vol. 20, No.2, pp. 227–241 (1982).
Narasimhan, R., Batta, R., and Karwan, M. H., “Routing Automated Guided Vehicles in the Presence of Interruptions,” International Journal of Production Research, Vol. 37, No.3, pp. 653–681 (February 1999).
O'Keefe, R. M. and Haddock, J., “Data-driven Generic Simulators for Flexible Manufacturing Systems,” International Journal of Production Research, Vol. 29, No.9, pp. 1795–1810 (1990).
O'Reilly, J. J. and Lilegdon, W. R., “Introduction to FACTOR/AIM,” 1999 Winter Simulation Conference Proceedings, P. A. Farrington, H. B. Nembhard, D. T. Sturrock, and G. W. Evans (Eds.), pp. 201–207 (December 1999).
Parunak, H. V. D., “Characterizing the Manufacturing Scheduling Problem,” Journal of Manufacturing Systems, Vol. 10, No.3, pp. 241–259 (1991).
Phillips, T., “Autosched AP by Autosimulations,” 1998 Winter Simulation Conference Proceedings, D. J. Medeiros, E. F. Watson, J. S. Carson, and M.S. Manivannan (Eds.), pp. 219–222 (December 1998).
Samadi, B., Morris, R. J. T., Rubin, L. D., Wong, W. S., and Ekroot, B. C., “The Operations Assistant: A New Manufacturing Resource Management Tool,” Journal of Manufacturing Systems, Vol. 9, No.4, pp. 303–314 (1990).
SAS Institute Inc., SAS User's Guide: Statistics, Version 5 Edition, SAS Institute Inc. Cary, NC (1985).
Smith, M. L., Ramesh, R., Dudek, R. A., and Blair, E. Z., “Characteristics of U.S. Flexible Manufacturing Systems—A Survey,” Proceedings of the Second ORSA/TIMS Conference on Flexible Manufacturing Systems: Operations Research Models and Applications, K. E. Stecke and R. Suri (Eds.), Elsevier Science Publishers B.V., Amsterdam, pp. 477–486 (1986).
Sule, D. R., “Heuristic Method of a Single Machine Scheduling Problem,” International Journal of Industrial Engineering—Applications and Practice, Vol. 1, pp. 167–174 (1994).
Suri, R. and Whitney, C. K., “Decision Support Requirements in Flexible Manufacturing,” Journal of Manufacturing Systems, Vol. 3, pp. 61–69 (1984).
Tayanithi, P., Manivannan, S., and Banks, J., “A Knowledge-Based Simulation Architecture to Analyze Interruptions in a Flexible Manufacturing System,” Journal of Manufacturing Systems, Vol. 11, No.3, pp. 195–214 (1992).
Tayanithi, P., Manivannan, S., and Banks, J., “Complexity Reduction During Interruption Analysis in a Flexible Manufacturing System using Knowledge-Based On-Line Simulation,” Journal of Manufacturing Systems, Vol. 12, No.2, pp. 153–169 (1993).
Vollmann, T. E., Berry, W. L., and Whybark, D. C., Manufacturing Planning and Control Systems (4th ed.), Irwin/McGraw-Hill, New York, pp. 540–545 (1997).
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Jain, S., Foley, W.J. Impact of Interruptions on Schedule Execution in Flexible Manufacturing Systems. International Journal of Flexible Manufacturing Systems 14, 319–344 (2002). https://doi.org/10.1023/A:1020952823369
Issue Date:
DOI: https://doi.org/10.1023/A:1020952823369