In the past few years, there has been an explosive surge in the use of machine learning (ML)
techniques to address combinatorial optimization (CO) problems, especially mixed-integer …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Learning to optimize is a rapidly growing area that aims to solve optimization problems or
improve existing optimization algorithms using machine learning (ML). In particular, the …

Cutting planes (cuts) are important for solving mixed-integer linear programs (MILPs), which
formulate a wide range of important real-world applications. Cut selection--which aims to …

Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …

The rapid advancement of artificial intelligence (AI) techniques has opened up new
opportunities to revolutionize various fields, including operations research (OR). This survey …

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed
railways to maintain punctuality and efficiency in the presence of unexpected disturbances …

In an era of digital ubiquity, efficient resource management and decision-making are
paramount across numerous industries. To this end, we present a comprehensive study on …

Graph neural networks (GNNs) have been widely used to predict properties and heuristics of
mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper …

Modern solvers for solving mixed integer programming (MIP) often rely on the branch-and-
bound (B&B) algorithm which could be of high time complexity, and presolving techniques …