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Scheduling under Uncertainty: Bounding the Makespan Distribution

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Computational Discrete Mathematics

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2122))

Abstract

Deterministic models for project scheduling and control suffer from the fact that they assume complete information and neglect random influences that occur during project execution. A typical consequence is the underestimation of the expected project duration and cost frequently observed in practice. This phenomenon occurs even in the absence of resource constraints, and has been the subject of extensive research in discrete mathematics and operations research.

This article presents a survey on the reasons for this phenomenon, its complexity, and on methods how to obtain more relevant information. this end, we consider scheduling models with fixed precedence constraints, but (independent) random processing times. The objective then is to obtain information about the distribution of the project makespan. We will demonstrate that this is an #\( \mathcal{P} \) -complete problem in general, and then consider several combinatorial methods to obtain approximate information about the makespan distribution.

Supported by Deutsche Forschungsgemeinschaft under grant Mo 346/3-3 and by German Israeli Foundation under grant I-564-246.06/97

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References

  1. V. Adlakha and V. Kulkarni. A classified bibliography of research on stochastic PERT networks: 1966-1987. INFOR, 27(3):272–296, 1989.

    MATH  Google Scholar 

  2. K. P. Anklesaria and Z. Drezner. A multivariate approach to estimating the completion time for PERT networks. J. Opl. Res. Soc., 40:811–815, 1986.

    Google Scholar 

  3. W. W. Bein, J. Kamburowski, and M. F. M. Stallmann. Optimal reduction of two-terminal directed acyclic graphs. SIAM J. Comput., 21:1112–1129, 1992.

    Article  MATH  MathSciNet  Google Scholar 

  4. B. Dodin. Determining the k most critical paths in PERT networks. Oper. Res., 32(859–877), 1984.

    Article  MATH  MathSciNet  Google Scholar 

  5. B. Dodin. Bounding the project completion time distribution in PERT networks. Oper. Res., 33:862–881, 1985.

    MATH  Google Scholar 

  6. D. R. Fulkerson. Expected critical path lengths in PERT networks. Oper. Res., 10:808–817, 1962.

    MATH  Google Scholar 

  7. D. I. Golenko. Statistische Methoden der Netzplantechnik. B. G. Teubner, Stuttgart, 1972.

    MATH  Google Scholar 

  8. J. N. Hagstrom. Computational complexity of PERT problems. Networks, 18:139–147, 1988.

    Article  MathSciNet  Google Scholar 

  9. U. Heller. On the shortest overall duration in stochastic project networks. Methods Oper. Res., 42:85–104, 1981.

    MATH  Google Scholar 

  10. G. Kleindorfer. Bounding distributions for a stochastic acyclic network. Oper. Res., 19:1586–1601, 1971.

    MATH  MathSciNet  Google Scholar 

  11. M. S. Krishnamoorthy and N. Deo. Complexity of the minimum-dummy-activities problem in a PERT network. Networks, 9:189–194, 1979.

    Article  MATH  MathSciNet  Google Scholar 

  12. A. Ludwig, R. H. Möhring, and F. Stork. A computational study on bounding the makespan distribution in stochastic project networks. Technical Report 609, Technische Universität Berlin, Fachbereich Mathematik, Berlin, Germany, 1998. appear in and Annals of Oper. Res.

    Google Scholar 

  13. J. J. Martin. Distribution of the time through a directed acyclic network. Oper. Res., 13:46–66, 1965.

    MATH  Google Scholar 

  14. I. Meilijson and A. Nadas. Convex majorization with an application to the length of critical paths. J. Appl. Prob., 16:671–677, 1979.

    Article  MATH  MathSciNet  Google Scholar 

  15. R. H. Möhring. Computationally tractable classes of ordered sets. In I. Rival, editor, Algorithms and Order, Nato Advanced Study Institutes Series, pages 105–193. D. Reidel Publishing Company, Dordrecht, 1989.

    Google Scholar 

  16. R. H. Möhring. Scheduling under uncertainty: Optimizing against a randomizing adversary. In K. Jansen and S. Khuller, editors, Approximation Algorithms for Combinatorial Optimization, Proceedings of the Third International Workshop APPROX 2000, Saarbrücken, pages 15–26. Springer-Verlag, Lecture Notes in Computer Science, vol. 1913, 2000.

    Chapter  Google Scholar 

  17. R. H. Möhring and R. Müller. A combinatorial approach to bound the distribution function of the makespan in stochastic project networks. Technical Report 610, Technische Universität Berlin, Fachbereich Mathematik, Berlin, Germany, 1998.

    Google Scholar 

  18. R. H. Möhring and F. J. Radermacher. The order-theoretic approach to scheduling: The stochastic case. In R. SOlowiński and J. Wceglarz, editors, Advances in Project Scheduling, pages 497–531. Elsevier Science B. V., Amsterdam, 1989.

    Google Scholar 

  19. V. Naumann. Measuring the distance to series-parallelity by path expressions. In E. W. Mayr, G. Schmidt, and G. Tinhofer, editors, Proceedings 20th International Workshop on Graph-Theoretic Concepts in Computer Science WG’94, pages 269–281. Springer-Verlag, Lecture Notes in Computer Science, vol. 903, 1995.

    Google Scholar 

  20. J. S. Provan and M. O. Ball. The complexity of counting cuts and of the probability that a graph is connected. SIAM J. Comput., 12:777–788, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  21. D. Sculli and Y. W. Shum. An approximate solution to the PERT problem. Computers and Mathematics with Applications, 21:1–7, 1991.

    Article  MATH  Google Scholar 

  22. A. W. Shogan. Bounding distributions for a stochastic PERT network. Networks, 7:359–381, 1977.

    Article  MATH  MathSciNet  Google Scholar 

  23. H. G. Spelde. Stochastische Netzpläne und ihre Anwendung im Baubetrieb. PhD thesis, Rheinisch-Westfälische Technische Hochschule Aachen, 1976.

    Google Scholar 

  24. K. Takamizawa, T. Nishizeki, and N. Saito. Linear-time computability of combinatorial problems on series-parallel graphs. J. Assoc. Mach., 29:623–641, 1982.

    MATH  MathSciNet  Google Scholar 

  25. J. Valdes, R. E. Tarjan, and E. L. Lawler. The recognition of series-parallel digraphs. SIAM J. Comput., 11:298–314, 1982.

    Article  MATH  MathSciNet  Google Scholar 

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Möhring, R.H. (2001). Scheduling under Uncertainty: Bounding the Makespan Distribution. In: Alt, H. (eds) Computational Discrete Mathematics. Lecture Notes in Computer Science, vol 2122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45506-X_7

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  • DOI: https://doi.org/10.1007/3-540-45506-X_7

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  • Print ISBN: 978-3-540-42775-9

  • Online ISBN: 978-3-540-45506-6

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