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, for example, a year, remains unstudied. Against this background, we jointly study a workforce planning problem that considers salaried drivers (SDs) and the temporal development of the crowdsourced driver (CD) fleet over a long-term time horizon. We consider two types of CDs, dedicated gig-drivers (DDs) and opportunistic gig-drivers (ODs). While DDs 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 SDs and CDs as well as stochastic CD fleet growth. We formulate the resulting workforce planning problem as a Markov decision process whose reward function reflects total costs, that is, wages and operational costs arising from serving demand with SDs and CDs, and solve it via approximate dynamic programming. Applying our approach to an environment based on real-world demand data from GrubHub, we find that in fleets consisting of SDs and CDs, approximate dynamic programming (ADP)-based hiring policies can outperform myopic hiring policies by up to 19 % and lookahead policies with perfect knowledge of future information by up to 10 % in total costs. In the studied setting, we observed that DDs 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 SDs increases.

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