The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

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

Recently, machine learning, particularly message-passing graph neural networks (MPNNs),
has gained traction in enhancing exact optimization algorithms. For example, MPNNs speed …

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 …

We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …

Mixed-integer programming (MIP) technology offers a generic way of formulating and
solving combinatorial optimization problems. While generally reliable, state-of-the-art MIP …

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

Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of
great importance for many practical applications. In this respect, the refinement heuristic …