Business

International trade

Operator (biology)

Value (mathematics)

Service (business)

Mathematics

Operations research

Transcription factor

Reinforcement learning

Gene

Mathematical optimization

Machine learning

Engineering

Marketing

Artificial intelligence

Repressor

Economics

Microeconomics

Profit (economics)

Chemistry

Fleet management

Retraining

Computer science

Artificial neural network

Biochemistry

Telecommunications

Transportation Science

We consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests. To solve this problem, we employ deep reinforcement learning, developing policies whose decision making uses [Formula: see text]-value approximations learned by deep neural networks. We compare these policies against a reoptimization-based policy and against dual bounds on the value of an optimal policy, including the value of an optimal policy with perfect information, which we establish using a Benders-based decomposition. We assess performance on instances derived from real data for the island of Manhattan in New York City. We find that, across instances of varying size, our best policy trained with deep reinforcement learning outperforms the reoptimization approach. We also provide evidence that this policy may be effectively scaled and deployed on larger instances without retraining.

Dynamic Ride-Hailing with Electric Vehicles