Abstract Energy Systems Modelling is growing in relevance on providing insights and
strategies to plan a carbon-neutral future. The implementation of an effective energy …

This paper surveys the recent attempts, both from the machine learning and operations
research communities, at leveraging machine learning to solve combinatorial optimization …

This paper surveys the machine learning literature and presents in an optimization
framework several commonly used machine learning approaches. Particularly …

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems
and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer …

Abstract Machine Learning (ML) can help solve combinatorial optimization (CO) problems
better. A popular approach is to use a neural net to compute on the parameters of a given …

The optimal design of compounds through manipulating properties at the molecular level is
often the key to considerable scientific advances and improved process systems …

Abstract In the Dial-a-Ride-Problem (DARP) a fleet of vehicles provides shared-ride services
to users specifying their origin, destination, and preferred arrival time. Typically, the problem …

We propose a method to approximate the solution of online mixed-integer optimization (MIO)
problems at very high speed using machine learning. By exploiting the repetitive nature of …

We present Ecole, a new library to simplify machine learning research for combinatorial
optimization. Ecole exposes several key decision tasks arising in general-purpose …

We introduce the idea that using optimal classification trees (OCTs) and optimal
classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms …