  We consider a meal delivery service fulfilling dynamic customer requests
given a set of couriers over the course of a day. A courier's duty is to
pick-up an order from a restaurant and deliver it to a customer. We model this
service as a Markov decision process and use deep reinforcement learning as the
solution approach. We experiment with the resulting policies on synthetic and
real-world datasets and compare those with the baseline policies. We also
examine the courier utilization for different numbers of couriers. In our
analysis, we specifically focus on the impact of the limited available
resources in the meal delivery problem. Furthermore, we investigate the effect
of intelligent order rejection and re-positioning of the couriers. Our
numerical experiments show that, by incorporating the geographical locations of
the restaurants, customers, and the depot, our model significantly improves the
overall service quality as characterized by the expected total reward and the
delivery times. Our results present valuable insights on both the courier
assignment process and the optimal number of couriers for different order
frequencies on a given day. The proposed model also shows a robust performance
under a variety of scenarios for real-world implementation.
