machine learning

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.

A prototype for the recommendation of treatment-resistant depression treatments (INFORMS)

A technical talk on a prototype for recommending treatments to patients suffering from treatment-resistant depression.

A prototype for the recommendation of treatment-resistant depression treatments (CORS)

A technical talk on a prototype for recommending treatments to patients suffering from treatment-resistant depression.

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

A technical talk on reinforcement learning for inventory control.

A branch-and-bound algorithm with machine learning for the open-shop scheduling problem (CORS)

A technical talk on a the use of machine learning to derive a new branching strategy for the open shop scheduling problem.

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 …