  Real-time vehicle dispatching operations in traditional car-sharing systems
is an already computationally challenging scheduling problem. Electrification
only exacerbates the computational difficulties as charge level constraints
come into play. To overcome this complexity, we employ an online minimum drift
plus penalty (MDPP) approach for SAEV systems that (i) does not require a
priori knowledge of customer arrival rates to the different parts of the system
(i.e. it is practical from a real-world deployment perspective), (ii) ensures
the stability of customer waiting times, (iii) ensures that the deviation of
dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a
computational time-complexity that allows for real-time implementation. Using
an agent-based simulator developed for SAEV systems, we test the MDPP approach
under two scenarios with real-world calibrated demand and charger
distributions: 1) a low-demand scenario with long trips, and 2) a high-demand
scenario with short trips. The comparisons with other algorithms under both
scenarios show that the proposed online MDPP outperforms all other algorithms
in terms of both reduced customer waiting times and vehicle dispatching costs.
