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.
Type de document :
Article dans une revue
international Jorunal of Interactive Multimedia and Artificial Intelligence, ImaI-Software, 2013, 2 (4), pp.67-75
Liste complète des métadonnées

https://hal.inria.fr/hal-00909296
Contributeur : Davide Sangiogi <>
Soumis le : mardi 26 novembre 2013 - 10:44:58
Dernière modification le : samedi 27 janvier 2018 - 01:30:57

Identifiants

  • HAL Id : hal-00909296, version 1

Collections

Citation

Roberto Amadini, Imane Sefrioui, Jacopo Mauro, Maurizio Gabbrielli. A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing. international Jorunal of Interactive Multimedia and Artificial Intelligence, ImaI-Software, 2013, 2 (4), pp.67-75. 〈hal-00909296〉

Partager

Métriques

Consultations de la notice

274