Abstract
This paper is about characterizing the expected makespan of a Partial Order Schedule (POS) under duration uncertainty. Our analysis is based on very general assumptions about the uncertainty: in particular, we assume that only the min, max, and average durations are known. This information is compatible with a whole range of values for the expected makespan. We prove that the largest of these values and the corresponding “worst-case” distribution can be obtained in polynomial time and we present an \(O(n^3)\) computation algorithm. Then, using theoretical and empirical arguments, we show that such expected makespan is strongly correlated with certain global properties of the POS, and we exploit this correlation to obtain a linear-time estimator. The estimator provides accurate results under a very large variety of POS structures, scheduling problem types, and uncertainty models. The algorithm and the estimator may be used during search by an optimization approach, in particular one based on Constraint Programming: this allows to tackle a stochastic problem by solving a dramatically simpler (and yet accurate) deterministic approximation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baptiste, P., Le Pape, C., Nuijten, W.: Constraint-based scheduling. Kluwer Academic Publishers (2001)
Bonfietti, A., Lombardi, M., Milano, M.: Disregarding duration uncertainty in partial order schedules? yes, we can!. In: Simonis, H. (ed.) CPAIOR 2014. LNCS, vol. 8451, pp. 210–225. Springer, Heidelberg (2014)
Cesta, A., Oddi, A., Smith, S.F.: Iterative flattening: a scalable method for solving multi-capacity scheduling problems. In: AAAI/IAAI, pp. 742–747 (2000)
Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack problems. Springer Science & Business Media (2004)
Kleywegt, A.J., Shapiro, A., Homem-de Mello, T.: The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization 12(2), 479–502 (2002)
Kolisch, R., Sprecher, A.: Psplib-a project scheduling problem library: Or software-orsep operations research software exchange program. European Journal of Operational Research 96(1), 205–216 (1997)
Laborie, P.: Complete MCS-based search: application to resource constrained project scheduling. In: Proc. of IJCAI, pp. 181–186. Professional Book Center (2005)
Laborie, P., Godard, D.: Self-adapting large neighborhood search: application to single-mode scheduling problems. In: Proc. of MISTA (2007)
Le Pape, C., Couronné, P., Vergamini, D., Gosselin, V.: Time-versus-capacity compromises in project scheduling. AISB Quarterly, 19 (1995)
Lombardi, M., Milano, M., Benini, L.: Robust scheduling of task graphs under execution time uncertainty. IEEE Trans. Computers 62(1), 98–111 (2013)
Morris, P., Muscettola, N., Vidal, T.: Dynamic control of plans with temporal uncertainty. In: Proc. of IJCAI, pp. 494–499. Morgan Kaufmann Publishers Inc. (2001)
Policella, N., Cesta, A., Oddi, A., Smith, S.F.: From precedence constraint posting to partial order schedules: A CSP approach to Robust Scheduling. AI Communications 20(3), 163–180 (2007)
Policella, N., Smith, S.F., Cesta, A., Oddi, A.: Generating robust schedules through temporal flexibility. In: Proc. of ICAPS, pp. 209–218 (2004)
Tarim, S.A., Manandhar, S., Walsh, T.: Stochastic constraint programming: A scenario-based approach. Constraints 11(1), 53–80 (2006)
Vidal, T.: Handling contingency in temporal constraint networks: from consistency to controllabilities. Journal of Experimental & Theoretical Artificial Intelligence 11(1), 23–45 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lombardi, M., Bonfietti, A., Milano, M. (2015). Deterministic Estimation of the Expected Makespan of a POS Under Duration Uncertainty. In: Pesant, G. (eds) Principles and Practice of Constraint Programming. CP 2015. Lecture Notes in Computer Science(), vol 9255. Springer, Cham. https://doi.org/10.1007/978-3-319-23219-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-23219-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23218-8
Online ISBN: 978-3-319-23219-5
eBook Packages: Computer ScienceComputer Science (R0)