Journal of Intelligent Manufacturing

, Volume 21, Issue 1, pp 49–64 | Cite as

Validating scheduling approaches against executional uncertainty

Article

Abstract

This paper introduces a general methodology to perform a comparative evaluation of different approaches to the problem of scheduling with uncertainty. Different proactive (off-line) and reactive (on-line) scheduling policies are evaluated by simulating the execution of a number of baseline schedules under uncertain environmental conditions, and observing the solution behaviors as such schedules get stressed by exogenous events. The analysis aims at assessing the impact of both proactive and reactive scheduling efforts on the robustness of the baseline solutions, against measurable disrupting factors, through reproducible experiments. As the results show, this dynamic approach reveals extremely useful to unveil some subtle aspects, which would have remained undetected through static metric evaluations.

Keywords

Scheduling with uncertainty Reactive scheduling Proactive scheduling Schedule execution 

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References

  1. Aloulou, M. A., & Portmann, M. C. (2003). An efficient proactive reactive scheduling approach to hedge against shop floor disturbances. In Proceedings of 1st Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA 2003) (pp. 337–362). The University of Nottingham Press.Google Scholar
  2. Aytug H., Lawley M.A., McKay K.N., Mohan S., Uzsoy R.M. (2005) Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research 165(1): 86–110CrossRefGoogle Scholar
  3. Bartusch M., Mohring R.H., Radermacher F.J. (1988) Scheduling project networks with resource constraints and time windows. Annals of Operations Research 16: 201–240CrossRefGoogle Scholar
  4. Cesta, A., Oddi, A., & Smith, S. F. (1998). Profile Based Algorithms to solve multiple capacitated metric scheduling problems. In Proceedings of the 4th International Conference on Artificial Intelligence Planning Systems, AIPS-98 (pp. 214–223). AAAI Press.Google Scholar
  5. Cesta, A., Oddi, A., & Smith, S. F. (1999). An iterative sampling procedure for resource constrained project scheduling with time windows. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (pp. 1022–1029). Morgan Kaufmann.Google Scholar
  6. Dechter R., Meiri I., Pearl J. (1991) Temporal constraints networks. Artificial Intelligence 49: 61–95CrossRefGoogle Scholar
  7. El Sakkout H.H., Wallace M.G. (2000) Probe backtrack search for minimal perturbation in dynamic scheduling. Constraints 5(4): 359–388CrossRefGoogle Scholar
  8. Ginsberg, M. L., Parkes, A. J., & Roy, A. (1998). Supermodels and robustness. In, Proceedings of the 15th National Conference on Artificial Intelligence, AAAI-98 (pp. 334–339). AAAI Press.Google Scholar
  9. Herroelen W., Leus R. (2004) Robust and reactive project scheduling: A review and classification of procedures. International Journal of Production Research 42(8): 1599–1620CrossRefGoogle Scholar
  10. Jensen M.T. (2001) Improving robustness and flexibility of tardiness and total flow-time job shops using robustness measures. Applied Soft Computing 1(1): 35–52CrossRefGoogle Scholar
  11. Kolisch R., Schwindt C., Sprecher A. (1998) Benchmark instances for project scheduling problems. In: Weglarz J. (eds) Project Scheduling—Recent Models, Algorithms and Applications. Kluwer Academic Publishers, Boston, pp 197–212Google Scholar
  12. Leon V., Wu S.D., Storer R.H. (1994) Robustness measures and robust scheduling for job shops. IIE Transactions 26(5): 32–43CrossRefGoogle Scholar
  13. Leus R., Herroelen W. (2004) Stability and resource allocation in project planning. IIE Transactions 36(7): 667–682CrossRefGoogle Scholar
  14. Mc Kay K.N., Safayeni F.R., Buzacott J.A. (1988) Job-shop scheduling theory: What is relevant?. Interfaces 18: 84–90CrossRefGoogle Scholar
  15. Policella N., Rasconi R. (2005) Designing a testset generator for reactive scheduling. Intelligenza Artificiale 2(3): 29–36Google Scholar
  16. Policella, N., Oddi, A., Smith, S. F., & Cesta, A. (2004a). Generating robust partial order schedules. In M. Wallace (Ed.), Principles and Practice of Constraint Programming, 10th International Conference, CP 2004, Lecture Notes in Computer Science (Vol. 3258, pp. 496–511). Springer.Google Scholar
  17. Policella, N., Smith, S. F., Cesta, A., & Oddi, A. (2004b). Generating robust schedules through temporal flexibility. In Proceedings of the 14th International Conference on Automated Planning & Scheduling, ICAPS’04 (pp. 209–218). AAAI.Google Scholar
  18. Roy B., Sussman B. (1964) Les problemes d’ordonnancement avec contraintes disjonctives, note DS n. 9 bis. SEMA, ParisGoogle Scholar
  19. Schwindt, C. (1998). A Branch and Bound Algorithm for the Resource-Constrained Project Duration Problem Subject to Temporal Constraints. Tech. Rep. WIOR-544, Institut für Wirtschaftstheorie und Operations Research, Universität Karlsruhe.Google Scholar
  20. Sevaux, M., & Sörensen, K. (2002). A genetic algorithm for robust schedules in a just-in-time environment. Tech. Rep. LAMIH/SP-2003-1, University of Valenciennes.Google Scholar
  21. Smith, S. F. (1994). OPIS: A Methodology and Architecture for Reactive Scheduling. In M. Fox, M. Zweben (Eds.), Intelligent Scheduling. Morgan Kaufmann.Google Scholar
  22. Wu S.D., Beyon E.S., Storer R.H. (1999) A graph-theoretic decomposition of the job shop scheduling problem to achieve scheduling robustness. Operations Research 47(1): 113–124CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Riccardo Rasconi
    • 1
  • Amedeo Cesta
    • 1
  • Nicola Policella
    • 2
  1. 1.Institute for Cognitive Science and TechnologyItalian National Research CouncilRomeItaly
  2. 2.European Space Agency, European Space Operations CenterDarmstadtGermany

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