While performing favourably on the independent and identically distributed (iid) instances,
most of the existing neural methods for vehicle routing problems (VRPs) struggle to …

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 …

Existing deep reinforcement learning (DRL)-based methods for solving the capacitated
vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in …

Existing neural heuristics often train a deep architecture from scratch for each specific
vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP …

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 …

Different variants of the Vehicle Routing Problem (VRP) have been studied for decades.
State-of-the-art methods based on local search have been developed for VRPs, while still …

Recent neural methods for vehicle routing problems always train and test the deep models
on the same instance distribution (ie, uniform). To tackle the consequent cross-distribution …

Motivated by the promising advances of deep-reinforcement learning (DRL) applied to
cooperative multi-agent systems we propose a model and learning procedure to solve the …

We present a novel deep reinforcement learning method to learn construction heuristics for
vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) …

Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical
applications. While previous heuristic or learning-based works achieve decent solutions on …