Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle
  Relocation and Pricing Decisions
  Large-scale ride-hailing systems often combine real-time routing at the
individual request level with a macroscopic Model Predictive Control (MPC)
optimization for dynamic pricing and vehicle relocation. The MPC relies on a
demand forecast and optimizes over a longer time horizon to compensate for the
myopic nature of the routing optimization. However, the longer horizon
increases computational complexity and forces the MPC to operate at coarser
spatial-temporal granularity, degrading the quality of its decisions. This
paper addresses these computational challenges by learning the MPC
optimization. The resulting machine-learning model then serves as the
optimization proxy and predicts its optimal solutions. This makes it possible
to use the MPC at higher spatial-temporal fidelity, since the optimizations can
be solved and learned offline. Experimental results show that the proposed
approach improves quality of service on challenging instances from the New York
City dataset.
