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Flow-Based Combinatorial Chance Constraints

  • Conference paper
Integration of AI and OR Techniques in Contraint Programming for Combinatorial Optimzation Problems (CPAIOR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7298))

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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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Authors and Affiliations

  1. Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Andre A. Cire, Elvin Coban & Willem-Jan van Hoeve

Authors
  1. Andre A. Cire
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  2. Elvin Coban
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  3. Willem-Jan van Hoeve
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Editor information

Editors and Affiliations

  1. École des Mines de Nantes, 4, rue Alfred Kastler, 44307, Nantes Cedex 3, France

    Nicolas Beldiceanu

  2. École des Mines de Nantes, 4 rue Alfred Kastler, 44307, Nantes Cedex 3, France

    Narendra Jussien

  3. Institut de Mathématiques Appliquées, 44 rue Rabelais, 49008, Angers Cedex 01, France

    Éric Pinson

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© 2012 Springer-Verlag Berlin Heidelberg

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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. https://doi.org/10.1007/978-3-642-29828-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-29828-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29827-1

  • Online ISBN: 978-3-642-29828-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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