Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives

X Wu, D Wang, L Wen, Y Xiao, C Wu, Y Wu… - arXiv preprint arXiv …, 2024 - arxiv.org
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …

Machine learning to solve vehicle routing problems: A survey

A Bogyrbayeva, M Meraliyev… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …

Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark

F Berto, C Hua, J Park, L Luttmann, Y Ma, F Bu… - arXiv preprint arXiv …, 2023 - arxiv.org
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …

Deep Reinforcement Learning for Solving Vehicle Routing Problems With Backhauls

C Wang, Z Cao, Y Wu, L Teng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The vehicle routing problem with backhauls (VRPBs) is a challenging problem commonly
studied in computer science and operations research. Featured by linehaul (or delivery) and …

Reinforcement learning-based nonautoregressive solver for traveling salesman problems

Y Xiao, D Wang, B Li, H Chen, W Pang… - … on Neural Networks …, 2024 - ieeexplore.ieee.org
The traveling salesman problem (TSP) is a well-known combinatorial optimization problem
(COP) with broad real-world applications. Recently, neural networks (NNs) have gained …

An edge-aware graph autoencoder trained on scale-imbalanced data for traveling salesman problems

S Liu, X Yan, Y Jin - Knowledge-Based Systems, 2024 - Elsevier
In recent years, there has been a notable surge in research on machine learning techniques
for combinatorial optimization. It has been shown that learning-based methods outperform …

PolyNet: Learning diverse solution strategies for neural combinatorial optimization

A Hottung, M Mahajan, K Tierney - arXiv preprint arXiv:2402.14048, 2024 - arxiv.org
Reinforcement learning-based methods for constructing solutions to combinatorial
optimization problems are rapidly approaching the performance of human-designed …

Risk control of epidemic in urban cold-chain transportation

S Liao, X Li, Y Niu, Z Xu, Y Cao - Sustainable Cities and Society, 2024 - Elsevier
The COVID-19 pandemic has severely disrupted the daily running of urban logistics system,
thereby increasing the resilience and health requirements in the design of sustainable …

Instance-Conditioned Adaptation for Large-scale Generalization of Neural Combinatorial Optimization

C Zhou, X Lin, Z Wang, X Tong, M Yuan… - arXiv preprint arXiv …, 2024 - arxiv.org
The neural combinatorial optimization (NCO) approach has shown great potential for solving
routing problems without the requirement of expert knowledge. However, existing …

Memory-Enhanced Neural Solvers for Efficient Adaptation in Combinatorial Optimization

F Chalumeau, R Shabe, N De Nicola… - arXiv preprint arXiv …, 2024 - arxiv.org
Combinatorial Optimization is crucial to numerous real-world applications, yet still presents
challenges due to its (NP-) hard nature. Amongst existing approaches, heuristics often offer …