Projects per year
Abstract
Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper makes three contributions to the field. Firstly we propose a metaheuristic approach to SCP that scales up to large problems better than stateof-the-art complete methods. Secondly we show how to use standard filtering algorithms to handle hard constraints more efficiently during search. Thirdly we extend our approach to problems with endogenous uncertainty, in which probability distributions are affected by decisions. This extension enables SCP to model and solve a wider class of problems.
Original language | English |
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Pages (from-to) | 57-76 |
Journal | Constraints |
Volume | 20 |
Issue number | 1 |
Early online date | 22 Aug 2014 |
DOIs | |
Publication status | Published - Jan 2015 |
Keywords / Materials (for Non-textual outputs)
- Stochastic constraint programming
- Metaheuristics
- Filtering
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Dive into the research topics of 'Hybrid Metaheuristics for Stochastic Constraint Programming'. Together they form a unique fingerprint.Projects
- 1 Finished
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Confidence-based optimization for inventory management at a local grocery store
Rossi, R. (Principal Investigator) & Yao, X. (Researcher)
1/09/13 → 31/08/14
Project: University Awarded Project Funding
Profiles
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Roberto Rossi
- Business School - Personal Chair of Uncertainty Modeling
- Management Science and Business Economics
- Edinburgh Strategic Resilience Initiative
- Culture, Accounting & Society Research Network
- Management Science
Person: Academic: Research Active