Recently, there is an emerging trend to apply deep reinforcement learning to solve the
vehicle routing problem (VRP), where a learnt policy governs the selection of next node for …

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) …

In this article, we study the reinforcement learning (RL) for vehicle routing problems (VRPs).
Recent works have shown that attention-based RL models outperform recurrent neural …

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

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

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 …

Abstract The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem in the
logistics domain, which is NP-hard. The objective is to dynamically schedule vehicles …

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

Multi-vehicle routing problem with soft time windows (MVRPSTW) is an indispensable
constituent in urban logistics distribution systems. Over the past decade, numerous methods …

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