Flow-Based Combinatorial Chance Constraints

  • Andre A. Cire
  • Elvin Coban
  • Willem-Jan van Hoeve
Conference paper

DOI: 10.1007/978-3-642-29828-8_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7298)
Cite this paper as:
Cire A.A., Coban E., van Hoeve WJ. (2012) Flow-Based Combinatorial Chance Constraints. In: Beldiceanu N., Jussien N., Pinson É. (eds) Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems. CPAIOR 2012. Lecture Notes in Computer Science, vol 7298. Springer, Berlin, Heidelberg

Abstract

We study stochastic variants of flow-based global constraints as combinatorial chance constraints. As a specific case study, we focus on the stochastic weighted alldifferent constraint. We first show that determining the consistency of this constraint is NP-hard. We then show how the combinatorial structure of the alldifferent constraint can be used to define chance-based filtering, and to compute a policy. Our propagation algorithm can be extended immediately to related flow-based constraints such as the weighted cardinality constraint. The main benefits of our approach are that our chance-constrained global constraints can be integrated naturally in classical deterministic CP systems, and are more scalable than existing approaches for stochastic constraint programming.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andre A. Cire
    • 1
  • Elvin Coban
    • 1
  • Willem-Jan van Hoeve
    • 1
  1. 1.Tepper School of BusinessCarnegie Mellon UniversityPittsburghUSA

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