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A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing

Abstract : Recent research in areas such as SAT solving and Integer Linear Programming has shown that the performances of a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. We report an empirical evaluation and comparison of portfolio approaches applied to Constraint Satisfaction Problems (CSPs). We compared models developed on top of off-the-shelf machine learning algorithms with respect to approaches used in the SAT field and adapted for CSPs, considering different portfolio sizes and using as evaluation metrics the number of solved problems and the time taken to solve them. Results indicate that the best SAT approaches have top performances also in the CSP field and are slightly more competitive than simple models built on top of classification algorithms.
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Contributor : Davide Sangiogi Connect in order to contact the contributor
Submitted on : Tuesday, November 26, 2013 - 10:44:58 AM
Last modification on : Wednesday, February 2, 2022 - 3:56:17 PM


  • HAL Id : hal-00909296, version 1



Roberto Amadini, Imane Sefrioui, Jacopo Mauro, Maurizio Gabbrielli. A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing. International Journal of Interactive Multimedia and Artificial Intelligence, 2013, 2 (4), pp.67-75. ⟨hal-00909296⟩



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