Summary of Neural Combinatorial Optimization Algorithms For Solving Vehicle Routing Problems: a Comprehensive Survey with Perspectives, by Xuan Wu et al.
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
by Xuan Wu, Di Wang, Lijie Wen, Yubin Xiao, Chunguo Wu, Yuesong Wu, Chaoyu Yu, Douglas L. Maskell, You Zhou
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract presents a comprehensive review of Neural Combinatorial Optimization (NCO) solvers specifically designed to solve Vehicle Routing Problems (VRPs). The authors divide NCO solvers into four categories: Learning to Construct, Learning to Improve, Learning to Predict-Once, and Learning to Predict-Multiplicity. The survey highlights the inadequacies of state-of-the-art (SOTA) solvers, including poor generalization, inability to solve large-scale VRPs, and difficulty in comparing with conventional algorithms. To overcome these limitations, the authors propose promising directions and compare representative NCO solvers from Reinforcement, Supervised, and Unsupervised Learning paradigms across small- and large-scale VRPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper reviews Neural Combinatorial Optimization (NCO) solvers for solving Vehicle Routing Problems (VRPs). It sorts these solvers into four types. The study shows that current best solvers have some big problems, like not working well on big problems or different kinds of VRP problems at the same time. To fix these issues, the authors suggest new ideas and compare some popular NCO solvers from three learning areas. |
Keywords
* Artificial intelligence * Generalization * Optimization * Supervised * Unsupervised
