Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …

Vehicle routing problems (VRP) are very important in many real-world applications and has
been studied for several decades. Recently, neural combinatorial optimization (NCO) has …

Despite the success of neural-based combinatorial optimization methods for end-to-end
heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we …

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …

We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …

Machine learning has been adapted to help solve NP-hard combinatorial optimization
problems. One prevalent way is learning to construct solutions by deep neural networks …

Despite the success of Neural Combinatorial Optimization methods for end-toend heuristic
learning, out-of-distribution generalization remains a challenge. In this paper, we present a …

Learning to solve vehicle routing problems (VRPs) has garnered much attention. However,
most neural solvers are only structured and trained independently on a specific problem …

Machine Learning-based heuristics have recently shown impressive performance in solving
a variety of hard combinatorial optimization problems (COPs). However they generally rely …

Deep graph generative modeling has gained enormous attraction in recent years due to its
impressive ability to directly learn the underlying hidden graph distribution. Despite their …