Operating A Three-sided Marketplace: Pricing, Spatial Staffing and Routing in Food Delivery Systems

Motivated by the proliferation of food delivery platforms, such as DoorDash, Uber Eats and Meituan, that match restaurants, customers and delivery drivers over a geographically dispersed network, we study the platform’s joint pricing, staffing and routing problem under endogenous participation of all three sides. Using a state-dependent queueing model where the service rate depends on the imbalance of the three sides due to spatial frictions, we study the equilibrium behavior of a large system in heavy traffic and show through asymptotic analysis how the platform controls balance capacity utilization and service quality. We show the platform’s value is threefold: (i) increased market output as the platform boosts demand for restaurants and offers faster delivery; (ii) delivery resource pooling that saves the restaurants’ logistic costs and increases deliverer utilization; (iii) efficient network routing that reduces cross-location pickups, hence customer waiting and deliverer idleness.