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Finding reliable solutions: event-driven probabilistic constraint programming

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  • S. Tarim
  • Brahim Hnich
  • Steven Prestwich
  • Roberto Rossi

Abstract

Real-life management decisions are usually made in uncertain environments, and decision support systems that ignore this uncertainty are unlikely to provide realistic guidance. We show that previous approaches fail to provide appropriate support for reasoning about reliability under uncertainty. We propose a new framework that addresses this issue by allowing logical dependencies between constraints. Reliability is then defined in terms of key constraints called “events”, which are related to other constraints via these dependencies. We illustrate our approach on three problems, contrast it with existing frameworks, and discuss future developments. Copyright Springer Science+Business Media, LLC 2009

Suggested Citation

  • S. Tarim & Brahim Hnich & Steven Prestwich & Roberto Rossi, 2009. "Finding reliable solutions: event-driven probabilistic constraint programming," Annals of Operations Research, Springer, vol. 171(1), pages 77-99, October.
    • Handle: RePEc:spr:annopr:v:171:y:2009:i:1:p:77-99:10.1007/s10479-008-0382-6
    DOI: 10.1007/s10479-008-0382-6
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    References listed on IDEAS

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    1. Liu, Baoding & Iwamura, Kakuzo, 1997. "Modelling stochastic decision systems using dependent-chance programming," European Journal of Operational Research, Elsevier, vol. 101(1), pages 193-203, August.
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    3. Herroelen, Willy & Leus, Roel, 2005. "Project scheduling under uncertainty: Survey and research potentials," European Journal of Operational Research, Elsevier, vol. 165(2), pages 289-306, September.
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    Cited by:

    1. W. A. Rijpkema & E. M. T. Hendrix & R. Rossi & J. G. A. J. Vorst, 2016. "Application of stochastic programming to reduce uncertainty in quality-based supply planning of slaughterhouses," Annals of Operations Research, Springer, vol. 239(2), pages 613-624, April.
    2. Roberto Rossi & S. Tarim & Brahim Hnich & Steven Prestwich, 2012. "Constraint programming for stochastic inventory systems under shortage cost," Annals of Operations Research, Springer, vol. 195(1), pages 49-71, May.

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