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Nowadays, logistics service providers (LSPs) increasingly consider using acrowdsourced workforce on the last mile to fulfill customers' expectationsregarding same-day or on-demand delivery at reduced costs. The crowdsourcedworkforce's availability is, however, uncertain. Therefore, LSPs often hireadditional fixed employees to perform deliveries when the availability ofcrowdsourced drivers is low. In this context, the reliability versusflexibility trade-off which LSPs face over a longer period, e.g., a year,remains unstudied. Against this background, we jointly study a workforceplanning problem that considers fixed drivers (FDs) and the temporaldevelopment of the crowdsourced driver (CD) fleet over a long-term timehorizon. We consider two types of CDs, gigworkers (GWs) and occasional drivers(ODs). While GWs are not sensitive to the request's destination and typicallyexhibit high availability, ODs only serve requests whose origin and destinationcoincide with their own private route's origin and destination. Moreover, toaccount for time horizon-specific dynamics, we consider stochastic turnover forboth FDs and CDs as well as stochastic CD fleet growth. We formulate theresulting workforce planning problem as a Markov decision process (MDP) whosereward function reflects total costs, i.e., wages and operational costs arisingfrom serving demand with FDs and CDs, and solve it via approximate dynamicprogramming (ADP). Applying our approach to an environment based on real-worlddemand 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% intotal costs. In the studied setting, we observed that GWs reduce the LSP'stotal costs more than ODs. When we account for CDs' increased resignationprobability when not being matched with enough requests, the amount of requiredFDs increases.

Strategic Workforce Planning in Crowdsourced Delivery with Hybrid Driver Fleets