Existing deep reinforcement learning (DRL)-based methods for solving the capacitated
vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in …

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

Learning how to automatically solve optimization problems has the potential to provide the
next big leap in optimization technology. The performance of automatically learned …

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 …

Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical
applications. While previous heuristic or learning-based works achieve decent solutions on …

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

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) …

ABSTRACT eCommerce, postal and logistics' planners require to solve large-scale
capacitated vehicle routing problems (CVRPs) on a daily basis. CVRP problems are NP …

Metaheuristics are widely employed to solve hard optimization problems, like vehicle routing
problems (VRP), for which exact solution methods are impractical. In particular, local search …

Reinforcement learning has recently shown promise in learning quality solutions in many
combinatorial optimization problems. In particular, the attention-based encoder-decoder …