  Nowadays, logistics service providers (LSPs) increasingly consider using a
crowdsourced workforce on the last mile to fulfill customers' expectations
regarding same-day or on-demand delivery at reduced costs. The crowdsourced
workforce's availability is, however, uncertain. Therefore, LSPs often hire
additional fixed employees to perform deliveries when the availability of
crowdsourced drivers is low. In this context, the reliability versus
flexibility trade-off which LSPs face over a longer period, e.g., a year,
remains unstudied. Against this background, we jointly study a workforce
planning problem that considers fixed drivers (FDs) and the temporal
development of the crowdsourced driver (CD) fleet over a long-term time
horizon. We consider two types of CDs, gigworkers (GWs) and occasional drivers
(ODs). While GWs are not sensitive to the request's destination and typically
exhibit high availability, ODs only serve requests whose origin and destination
coincide with their own private route's origin and destination. Moreover, to
account for time horizon-specific dynamics, we consider stochastic turnover for
both FDs and CDs as well as stochastic CD fleet growth. We formulate the
resulting workforce planning problem as a Markov decision process (MDP) whose
reward function reflects total costs, i.e., wages and operational costs arising
from serving demand with FDs and CDs, and solve it via approximate dynamic
programming (ADP). Applying our approach to an environment based on real-world
demand data from GrubHub, we find that in fleets consisting of FDs and CDs,
ADP-based hiring policies can outperform myopic hiring policies by up to 19% in
total costs. In the studied setting, we observed that GWs reduce the LSP's
total costs more than ODs. When we account for CDs' increased resignation
probability when not being matched with enough requests, the amount of required
FDs increases.
