reinforcement learning

Dynamic ride-hailing with electric vehicles

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 …

Solving multi-echelon inventory problems with heuristic-guided deep reinforcement learning and centralized control (CORS)

A technical talk on reinforcement learning for inventory control.

Solving multi-echelon inventory problems with heuristic-guided deep reinforcement learning and centralized control (JOPT)

A technical talk on reinforcement learning for inventory control.

Integration of machine learning and operations research to solve more realistic problems (CIRRELT)

An overview talk of past and current research.

Solving multi-echelon inventory problems with heuristic-guided deep reinforcement learning and centralized control

Multi-echelon inventory models aim to minimize the system-wide total cost in a multi-stage supply chain by applying a proper ordering policy to each of the stages. In a practical inventory system when backlog costs can be incurred in multiple stages, …

Solving multi-echelon inventory management problems with deep reinforcement learning (IWLS)

A technical talk on reinforcement learning for inventory control.

Solving multi-echelon inventory problems with deep reinforcement learning (CORS)

A technical talk on reinforcement learning for inventory control.

Atari-fying the vehicle routing problem with stochastic service requests (CORS)

A technical talk on the new paradigm of atari-fying operations research problems.

Atari-fying the vehicle routing problem with stochastic service requests

We present a new general approach to modeling research problems as Atari-like videogames to make them amenable to recent groundbreaking solution methods from the deep reinforcement learning community. The approach is flexible, applicable to a wide …