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Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem

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Learning and Intelligent Optimization (LION 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7997))

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Abstract

Constraint Programming (CP) solvers classically explore the solution space using tree search-based heuristics. Monte-Carlo Tree-Search (MCTS), a tree-search based method aimed at sequential decision making under uncertainty, simultaneously estimates the reward associated to the sub-trees, and gradually biases the exploration toward the most promising regions. This paper examines the tight combination of MCTS and CP on the job shop problem (JSP). The contribution is twofold. Firstly, a reward function compliant with the CP setting is proposed. Secondly, a biased MCTS node-selection rule based on this reward is proposed, that is suitable in a multiple-restarts context. Its integration within the Gecode constraint solver is shown to compete with JSP-specific CP approaches on difficult JSP instances.

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Author information

Authors and Affiliations

  1. Microsoft Research, Cambridge, UK

    Manuel Loth & Youssef Hamadi

  2. TAO, CNRS–INRIA–LRI, Université Paris-Sud, Orsay, France

    Manuel Loth, Michéle Sebag & Marc Schoenauer

  3. School of ICT, KTH Royal Institute of Technology, Stockholm, Sweden

    Christian Schulte

Authors
  1. Manuel Loth
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  2. Michéle Sebag
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  3. Youssef Hamadi
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  4. Marc Schoenauer
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  5. Christian Schulte
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Corresponding author

Correspondence to Manuel Loth .

Editor information

Editors and Affiliations

  1. Università Catania Dipto. Matematica e Informatica, Catania, Italy

    Giuseppe Nicosia

  2. Industrial & Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos Pardalos

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Loth, M., Sebag, M., Hamadi, Y., Schoenauer, M., Schulte, C. (2013). Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_35

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  • DOI: https://doi.org/10.1007/978-3-642-44973-4_35

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44972-7

  • Online ISBN: 978-3-642-44973-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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