  Ride-hailing services require efficient optimization algorithms to
simultaneously plan routes and pool users in shared rides. We consider a static
dial-a-ride problem (DARP) where a series of origin-destination requests have
to be assigned to routes of a fleet of vehicles. Thereby, all requests have
associated time windows for pick-up and delivery, and may be denied if they can
not be serviced in reasonable time or at reasonable cost. Rather than using a
spatial representation of the transportation network we suggest an event-based
formulation of the problem. While the corresponding MILP formulations require
more variables than standard models, they have the advantage that capacity,
pairing and precedence constraints are handled implicitly. The approach is
tested and validated using a standard IP-solver on benchmark data from the
literature. Moreover, the impact of, and the trade-off between, different
optimization goals is evaluated on a case study in the city of Wuppertal
(Germany).
