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Evaluating periodic rescheduling policies using a rolling horizon framework in an industrial-scale multipurpose plant

  • Zachariah Stevenson
  • Ricardo FukasawaEmail author
  • Luis Ricardez-Sandoval
Article
  • 25 Downloads

Abstract

Periodic rescheduling is a commonly used method for scheduling short-term operations. Through computational experiments that vary plant parameters, such as the load and the capacity of a facility, we investigate the effects these parameters have on plant performance under periodic rescheduling. The results show that choosing a suitable rescheduling policy depends highly on some key plant parameters. In particular, by modifying various parameters of the facility, the performance ranking of the various rescheduling policies may be reversed compared to the results obtained with nominal parameter values. This highlights the need to consider both facility characteristics and what the crucial objective of the facility is when selecting a rescheduling policy. This study considers a variant of the job shop problem, used to model the operation of an industrial-scale analytical services facility using different periodic rescheduling policies. A rolling horizon routine is used to schedule operations over the scheduling horizon. Performance is measured in terms of job throughput, job makespan, and proportion of jobs on time at the end of the scheduling horizon to obtain a more complete understanding of how performance varies between rescheduling policies.

Keywords

Scheduling Rescheduling Fixed Periodic Frequency 

Notes

Acknowledgements

The financial support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC) (CRDPJ 468517-14), Ontario Centers for Excellence (OCE), and the industrial partner in the analytical services sector is gratefully acknowledged.

Supplementary material

10951_2019_627_MOESM1_ESM.pdf (4.8 mb)
Supplementary material 1 (pdf 4921 KB)

References

  1. Baykasoğlu, A., & Karaslan, F. S. (2017). Solving comprehensive dynamic job shop scheduling problem by using a grasp-based approach. International Journal of Production Research, 55(11), 3308–3325.  https://doi.org/10.1080/00207543.2017.1306134.CrossRefGoogle Scholar
  2. Church, L. K., & Uzsoy, R. (1992). Analysis of periodic and event-driven rescheduling policies in dynamic shops. International Journal of Computer Integrated Manufacturing, 5(3), 153–163.  https://doi.org/10.1080/09511929208944524.CrossRefGoogle Scholar
  3. Dolan, E. D., & More, J. J. (2002). Benchmarking optimization software with performance profiles. Mathematical Programming, 91(2), 201–213.  https://doi.org/10.1007/s101070100263.CrossRefGoogle Scholar
  4. Dunning, I., Huchette, J., & Lubin, M. (2017). Jump: A modeling language for mathematical optimization. SIAM Review, 59(2), 295–320.  https://doi.org/10.1137/15M1020575.CrossRefGoogle Scholar
  5. Gupta, D., & Maravelias, C. (2016). On deterministic online scheduling: Major considerations, paradoxes and remedies. Computers and Chemical Engineering, 94(2), 312–330.  https://doi.org/10.1016/j.compchemeng.2016.08.006.CrossRefGoogle Scholar
  6. Hozak, K., & Hill, J. A. (2009). Issues and opportunities regarding replanning and rescheduling frequencies. International Journal of Production Research, 47(18), 4955–4970.  https://doi.org/10.1080/00207540802047106.CrossRefGoogle Scholar
  7. Kim, M. H., & Kim, Y.-D. (1994). Simulation-based real-time scheduling in a flexible manufacturing system. Journal of Manufacturing Systems, 13(2), 85–93.  https://doi.org/10.1016/0278-6125(94)90024-8.CrossRefGoogle Scholar
  8. Koller, R., Ricardez-Sandoval, L., & Biegler, L. (2018). Stochastic back-off algorithm for simultaneous design, control, and scheduling of multiproduct systems under uncertainty. AIChE Journal, 64(7), 2379–2389.  https://doi.org/10.1002/aic.16092.CrossRefGoogle Scholar
  9. Lagzi, S., Yeon Lee, D., Fukasawa, R., & Ricardez-Sandoval, L. (2017). A computational study of continuous and discrete time formulations for a class of short-term scheduling problems for multipurpose plants. Industrial & Engineering Chemistry Research, 56(31), 8940–8953.  https://doi.org/10.1021/acs.iecr.7b01718.CrossRefGoogle Scholar
  10. Muhlemann, A. P., Lockett, A. G., & Farn, C.-K. (1982). Job shop scheduling heuristics and frequency of scheduling. International Journal of Production Research, 29(2), 227–241.  https://doi.org/10.1080/00207548208947763.CrossRefGoogle Scholar
  11. Ouelhadj, D., & Petrovic, S. (2008). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12, 417–431.  https://doi.org/10.1007/s10951-008-0090-8.CrossRefGoogle Scholar
  12. Pfund, M. E., & Fowler, J. W. (2017). Extending the boundaries between scheduling and dispatching: Hedging and rescheduling techniques. International Journal of Production Research, 55(11), 3294–3307.  https://doi.org/10.1080/00207543.2017.1306133.CrossRefGoogle Scholar
  13. Sabuncuoglu, I., & Karabuk, S. (1999). Rescheduling frequency in an FMS with uncertain processing times and unreliable machines. Journal of Manufacturing Systems, 18(4), 268–283.  https://doi.org/10.1016/S0278-6125(00)86630-3.CrossRefGoogle Scholar
  14. Sabuncuoglu, I., & Kizilisik, O. B. (2003). Reactive scheduling in a dynamic and stochastic FMS environment. International Journal of Production Research, 41(17), 4211–4231.  https://doi.org/10.1080/0020754031000149202.CrossRefGoogle Scholar
  15. Shafaei, R., & Brunn, P. (1999). Workshop scheduling using practical (inaccurate) data part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach. International Journal of Production Research, 37(17), 3913–3925.  https://doi.org/10.1080/002075499189682.CrossRefGoogle Scholar
  16. Vieira, G. E., Herrmann, J. W., & Lin, E. (2000a). Analytical models to predict the performance of a single-machine system under periodic and event-driven rescheduling strategies. International Journal of Production Research, 38(8), 1899–1915.  https://doi.org/10.1080/002075400188654.CrossRefGoogle Scholar
  17. Vieira, G. E., Herrmann, J. W., & Lin, E. (2000b). Predicting the performance of rescheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256–266.  https://doi.org/10.1016/S0278-6125(01)80005-4.CrossRefGoogle Scholar
  18. Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: A framework of strategies, policies, and methods. Journal of Scheduling, 6(1), 39–62.  https://doi.org/10.1023/A:1022235519958.CrossRefGoogle Scholar
  19. Yano, C. A., & Carlson, R. C. (1987). Interaction between frequency of rescheduling and the role of safety stock in material requirements for planning systems. International Journal of Production Research, 25(2), 221–232.  https://doi.org/10.1080/00207548708919835.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada

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