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 …

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 …

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 …

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

This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both …

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) …

Many real-world vehicle routing problems involve rich sets of constraints with respect to the
capacities of the vehicles, time windows for customers etc. While in recent years first …

Recent researches show that machine learning has the potential to learn better heuristics
than the one designed by human for solving combinatorial optimization problems. The deep …

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 …

This paper is concerned with solving combinatorial optimization problems, in particular, the
capacitated vehicle routing problems (CVRP). Classical Operations Research (OR) …