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 …

Decision-focused learning (DFL) is an emerging paradigm that integrates machine learning
(ML) and constrained optimization to enhance decision quality by training ML models in an …

In deterministic optimization, it is typically assumed that all problem parameters are fixed
and known. In practice, however, some parameters may be a priori unknown but can be …

In the last years decision-focused learning framework, also known as predict-and-optimize,
have received increasing attention. In this setting, the predictions of a machine learning …

Abstract Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large
number of combinatorial optimization problems. Recently, it has been shown that Large …

Abstract Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a
downstream optimization task that uses its predictions in order to perform better\textit {on that …

Decision-focused learning (DFL) was recently proposed for stochastic optimization problems
that involve unknown parameters. By integrating predictive modeling with an implicitly …

Combinatorial optimization (CO) layers in machine learning (ML) pipelines are a powerful
tool to tackle data-driven decision tasks, but they come with two main challenges. First, the …

Port state control (PSC) serves as the final defense against substandard ships in maritime
transportation. The port state control officer (PSCO) routing problem involves selecting ships …

The development of the Internet of things (IoT) and online platforms enables companies and
governments to collect data from a much broader spatial and temporal area in the logistics …