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 the Sioux Falls network to illustrate the performance of the proposed algorithm.
langue originale | Anglais |
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Pages (de - à) | 631-645 |
journal | Journal of Information Science and Engineering |
Volume | 29 |
Numéro de publication | 4 |
état | Publié - 2013 |
Modification externe | Oui |