Hybrid metaheuristics for stochastic constraint programming

S. D. Prestwich; S. A. Tarim; R. Rossi; B. Hnich

doi:10.1007/s10601-014-9170-x

Hybrid metaheuristics for stochastic constraint programming

S. D. Prestwich
, S. A. Tarim
, R. Rossi
, B. Hnich

Hacettepe University
University of Edinburgh
Izmir Ekonomi University

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	57-76
Number of pages	20
Journal	Constraints
Volume	20
Issue number	1
DOIs	https://doi.org/10.1007/s10601-014-9170-x
Publication status	Published - Jan 2014

UCC Futures

Artificial Intelligence and Data Analytics

Keywords

Filtering
Metaheuristics
Stochastic constraint programming

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10.1007/s10601-014-9170-x

Cite this

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title = "Hybrid metaheuristics for stochastic constraint programming",

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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doi = "10.1007/s10601-014-9170-x",

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AB - 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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KW - Metaheuristics

KW - Stochastic constraint programming

UR - https://www.scopus.com/pages/publications/85027950224

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Hybrid metaheuristics for stochastic constraint programming

Abstract

UCC Futures

Keywords

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