  With the advent of self-driving cars, experts envision autonomous
mobility-on-demand services in the near future to cope with overloaded
transportation systems in cities worldwide. Efficient operations are imperative
to unlock such a system's maximum improvement potential. Existing approaches
either consider a narrow planning horizon or ignore essential characteristics
of the underlying problem. In this paper, we develop an algorithmic framework
that allows the study of very large-scale pickup and delivery routing problems
with more than 20 thousand requests, which arise in the context of integrated
request pooling and vehicle-to-request dispatching. We conduct a computational
study and present comparative results showing the characteristics of the
developed approaches. Furthermore, we apply our algorithm to related benchmark
instances from the literature to show the efficacy. Finally, we solve very
large-scale instances and derive insights on upper-bound improvements regarding
fleet sizing and customer delay acceptance from a practical perspective.
