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