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Hybrid metaheuristics for stochastic constraint programming

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Abstract

Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods, and exploits standard filtering algorithms to handle hard constraints more efficiently. For problems with many scenarios it can be combined with scenario reduction and sampling methods.

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Authors and Affiliations

  1. Insight Centre for Data Analytics, University College Cork, Cork, Ireland

    S. D. Prestwich

  2. Institute of Population Studies, Hacettepe University, Ankara, Turkey

    S. A. Tarim

  3. University of Edinburgh Business School, Edinburgh, UK

    R. Rossi

  4. Department of Computer Engineering, Izmir University of Economics, Izmir, Turkey

    B. Hnich

Authors
  1. S. D. Prestwich
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  2. S. A. Tarim
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  3. R. Rossi
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  4. B. Hnich
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Correspondence to S. D. Prestwich.

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Prestwich, S.D., Tarim, S.A., Rossi, R. et al. Hybrid metaheuristics for stochastic constraint programming. Constraints 20, 57–76 (2015). https://doi.org/10.1007/s10601-014-9170-x

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