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 …

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 problem (VRP) is a well known NP-hard combinatorial optimization problem
having several variants. In this paper, we consider VRP along with additional constraints of …

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 …

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 …

With the rise of e-commerce and increasing customer requirements, logistics service
providers face a new complexity in their daily planning, mainly due to efficiently handling …

We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using
reinforcement learning. In this approach, we train a single policy model that finds near …

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 …

This paper develops an inherently parallelised, fast, approximate learning-based solution to
the generic class of Capacitated Vehicle Routing Problems with Time Windows and …

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 …