Routing problems are very important in intelligent transportation systems. Recently, a
number of deep learning-based methods are proposed to automatically learn construction …

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 …

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) …

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing
problems (VRPs). However, it is less effective in learning improvement models for VRP …

Recent studies in using deep learning (DL) to solve routing problems focus on construction
heuristics, whose solutions are still far from optimality. Improvement heuristics have great …

Recent works using deep learning to solve routing problems such as the traveling salesman
problem (TSP) have focused on learning construction heuristics. Such approaches find good …

For NP-hard combinatorial optimization problems, it is usually challenging to find high-
quality solutions in polynomial time. Designing either an exact algorithm or an approximate …

Online vehicle routing is an important task of the modern transportation service provider.
Contributed by the ever-increasing real-time demand on the transportation system …

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 …

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 …