Workforce planning for O2O delivery systems with crowdsourced drivers

This paper proposes a workforce capacity planning model for online-to-offline (O2O) logistics systems. Three types of workforces with different compensation schemes are considered: in-house drivers, full-time crowdsourced drivers, and part-time crowdsourced drivers. We propose a cost minimization problem to determine the optimal workforce capacity and optimal order allocations, considering the dynamics of incoming demand. We apply a dataset from an O2O platform and our analysis reveals that (1) the capacity plan priority is part-time crowdsourced drivers, followed by full-time crowdsourced drivers and in-house drivers; the order assignment priority is reverse; (2) setting a proper guaranteed minimum order level and using the single service mode for full-time crowdsourced drivers can significantly reduce the rate of unfulfilled orders and total cost; and (3) leveraging the flexibility of the part-time crowdsourced drivers can significantly reduce the unfulfilled orders and total cost. Moreover, customizing the design of these schemes further enhances their potential. We expect these results to shed light on cost control and provide a model for crowd-sourcing which can improve the efficiency of O2O on-demand businesses.