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/

Zendy Plus

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

Zendy Tools

Zendy Open

Despite the significant advances in vehicle automation and electrification,the next-decade aspirations for massive deployments of autonomous electricmobility on demand (AEMoD) services are still threatened by two majorbottlenecks, namely the computational and charging delays. This paper proposesa solution for these two challenges by suggesting the use of fog computing forAEMoD systems, and developing an optimized charging scheme for its vehicleswith and multi-class dispatching scheme for the customers. A queuing modelrepresenting the proposed multi-class management scheme with sub-class serviceis first introduced. The stability conditions of the system in a given cityzone are then derived. Decisions on the proportions of each class vehicles topartially/fully charge, or directly serve customers of possible sub-classes arethen optimized in order to minimize the maximum response time of the system.Results show the merits of our optimized model compared to a previouslyproposed scheme and other non-optimized policies.

Multi-Class Management with Sub-Class Service for Autonomous Electric Mobility On-Demand Systems