English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper proposes a hybrid multiagent learning algorithm for solving thedynamic simulation-based bilevel network design problem. The objective is todetermine the op-timal frequency of a multimodal transit network, whichminimizes total users' travel cost and operation cost of transit lines. Theproblem is formulated as a bilevel programming problem with equilibriumconstraints describing non-cooperative Nash equilibrium in a dynamicsimulation-based transit assignment context. A hybrid algorithm combing thecross entropy multiagent learning algorithm and Hooke-Jeeves algorithm isproposed. Computational results are provided on the Sioux Falls network toillustrate the perform-ance of the proposed algorithm.

A hybrid cross entropy algorithm for solving dynamic transit network design problem