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 …

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 …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …

Combinatorial Optimization underpins many real-world applications and yet, designing
performant algorithms to solve these complex, typically NP-hard, problems remains 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 …

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 …

Recent deep models for solving routing problems always assume a single distribution of
nodes for training, which severely impairs their cross-distribution generalization ability. In …

Open-source reinforcement learning (RL) environments have played a crucial role in driving
progress in the development of AI algorithms. In modern RL research, there is a need for …

Generalization, ie, the ability of solving problem instances that are not available during the
system design and development phase, is a critical goal for intelligent systems. A typical way …

This work presents a new metaheuristic for the euclidean Traveling Salesman Problem
(TSP) based on an Anytime Automatic Algorithm Selection model using a portfolio of five …