Recently, neural heuristics based on deep learning have reported encouraging results for
solving vehicle routing problems (VRPs), especially on independent and identically …

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 …

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

Our model is based on the graph convolutional network (GCN) with node feature
(coordination and demand) and edge feature (the real distance between nodes) as input …

The vehicle routing problem as a classic NP-hard problem could be optimized by path
choices due to its practical application value. This study proposes a novel variational …

This paper presents an approach to learn the local-search heuristics that iteratively improves
the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a …

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

Neural solvers based on attention mechanism have demonstrated remarkable effectiveness
in solving vehicle routing problems. However, in the generalization process from small scale …

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 …

While the encoder–decoder structure is widely used in the recent neural construction
methods for learning to solve vehicle routing problems (VRPs), they are less effective in …