Résumé
This paper proposes a hybrid multiagent learning algorithm for solving the dynamic simulation-based bilevel network design problem. The objective is to determine the optimal frequency of a multimodal transit network, which minimizes total users' travel cost and operation cost of transit lines. The problem is formulated as a bilevel programming problem with equilibrium constraints describing non-cooperative Nash equilibrium in a dynamic simulation-based transit assignment context. A hybrid algorithm combing the cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is proposed. Computational results are provided on a small network to illustrate the performance of the proposed algorithm.
langue originale | Anglais |
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titre | 2011 International Conference on Technologies and Applications of Artificial Intelligence |
Sous-titre | 11-13 Nov. 2011 |
rédacteurs en chef | Chung Li |
Lieu de publication | Taiwan |
Editeur | IEEE Computer Society |
Pages | 113-118 |
Nombre de pages | 6 |
Les DOIs | |
état | Publié - 2011 |
Modification externe | Oui |