Large-scale ride-sharing systems combine real-time dispatching and routing optimization over a rolling time horizon with a model predictive control (MPC) component that relocates idle vehicles to anticipate the demand. The MPC optimization operates over a longer time horizon to compensate for the inherent myopic nature of the real-time dispatching. These longer time horizons are beneficial for the quality of the decisions but increase computational complexity.  To address this computational challenge, this paper proposes a hybrid approach that combines machine learning and optimization. The machine-learning component learns the optimal solution to the MPC optimization on the aggregated level to overcome the sparsity and high-dimensionality of the MPC solutions . The optimization component transforms the machine-learning predictions back to the original granularity via a tractable transportation model. As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization  . Experimental results show that the hybrid approach achieves 27\% further reduction in rider waiting time than the MPC optimization , thanks to its ability to model a longer time horizonwithin the computational limits .
Large-scale ride-sharing systems combine real-time dispatching and routing optimization over a rolling time horizon with a model predictive control (MPC) component that relocates idle vehicles to anticipate the demand. The MPC optimization operates over a longer time horizon to compensate for the inherent myopic nature of the real-time dispatching. These longer time horizons are beneficial for the quality of relocation decisions but increase computational complexity. Consequently, the ride-sharing operators are often forced to use a relatively short time horizon. To address this computational challenge, this paper proposes a hybrid approach that combines machine learning and optimization. The machine-learning component learns the optimal solution to the MPC on the aggregated level to overcome the sparsity and high-dimensionality of the solution . The optimization component transforms the machine-learning prediction back to the original granularity through a tractable transportation model. As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization , which allows the ride-sharing operators to consider a longer time horizon . Experimental results show that the hybrid approach achieves significantly better service quality than the MPC optimization in terms of average rider waiting time, due to its ability to model a longer horizon .
R
the
relocation
A
Consequently, the ride-sharing operators are often forced to use a relatively short time horizon.
D
optimization
R
MPC solutions
solution
R
predictions
prediction
R
via
through
A
, which allows the ride-sharing operators to consider a longer time horizon
R
27\% further reduction in rider waiting time
significantly better service quality
R
, thanks
in terms of average rider waiting time, due
R
time horizonwithin the computational limits
horizon