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

Local search has been established as a successful cornerstone to tackle the Vehicle
Routing Problem, and is included in many state-of-the-art heuristics. In this paper we aim to …

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 …

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 …

Green vehicle routing problem (GVRP) aims to consider greenhouse gas emissions
reduction, while routing the vehicles. It can be either through adopting Alternative Fuel …

In the modern supply chain system, large-scale transportation tasks require the collaborative
work of multiple vehicles to be completed on time. Over the past few decades, multi-vehicle …

Vehicle routing problem with time windows (VRPTW) contains two crucial objectives:
minimizing the number of vehicles and minimizing the total travel distance. However, most …

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 …

An increasing number of companies focus on customer satisfaction to increase the lifetime
value of each customer. In vehicle routing, customer satisfaction is often a result of …

Vehicle routing problems (VRPs) have been the subject of intense and fast-growing
research for the last six decades. These problems have received much attention in …