Recently there has been a surge of interest in operations research (OR) and the machine
learning (ML) community in combining prediction algorithms and optimization techniques to …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Decision trees (DTs) epitomize the ideal of interpretability of machine learning (ML) models.
The interpretability of decision trees motivates explainability approaches by so-called …

Optimization is a ubiquitous modeling tool and is often deployed in settings which
repeatedly solve similar instances of the same problem. Amortized optimization methods …

Decision trees are among the most popular machine learning models and are used routinely
in applications ranging from revenue management and medicine to bioinformatics. In this …

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 an integrated prediction-optimization (PredOpt) framework to efficiently solve
sequential decision-making problems by predicting the values of binary decision variables …

The concept of a Digital Twin (DT) has stood out among the emerging digitization
technologies and been embraced by US and EU governments and companies. Practitioners …

Plug-in electric vehicles (PEVs) have the highest promise for dramatically reducing
transportation emissions. No other option has comparable emission reduction potential or as …

We study an inventory optimization problem for a retailer that faces stochastic online and in-
store demand in a selling season of fixed length. The retailer has to decide the initial …