Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy

by   Chengrui Gao, et al.
HUAWEI Technologies Co., Ltd.
Nanjing University

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, which has been receiving more and more attention due to the high efficiency and less requirement for expert knowledge. However, many neural construction methods for Vehicle Routing Problems (VRPs) focus on synthetic problem instances with limited scales and specified node distributions, leading to poor performance on real-world problems which usually involve large scales together with complex and unknown node distributions. To make neural VRP solvers more practical in real-world scenarios, we design an auxiliary policy that learns from the local transferable topological features, named local policy, and integrate it with a typical constructive policy (which learns from the global information of VRP instances) to form an ensemble policy. With joint training, the aggregated policies perform cooperatively and complementarily to boost generalization. The experimental results on two well-known benchmarks, TSPLIB and CVRPLIB, of travelling salesman problem and capacitated VRP show that the ensemble policy consistently achieves better generalization than state-of-the-art construction methods and even works well on real-world problems with several thousand nodes.


page 1

page 2

page 3

page 4


A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems

Recent researches show that machine learning has the potential to learn ...

BQ-NCO: Bisimulation Quotienting for Generalizable Neural Combinatorial Optimization

Despite the success of Neural Combinatorial Optimization methods for end...

Learning TSP Requires Rethinking Generalization

End-to-end training of neural network solvers for combinatorial problems...

A Parallel Ensemble of Metaheuristic Solvers for the Traveling Salesman Problem

The travelling salesman problem (TSP) is one of the well-studied NP-hard...

Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer

Recently, Transformer has become a prevailing deep architecture for solv...

RP-DQN: An application of Q-Learning to Vehicle Routing Problems

In this paper we present a new approach to tackle complex routing proble...

Solve routing problems with a residual edge-graph attention neural network

For NP-hard combinatorial optimization problems, it is usually difficult...

Please sign up or login with your details

Forgot password? Click here to reset