用图神经网络解决车辆路径优化问题，提升求解效率。
Neural Networks for Vehicle Routing Problem
- 设计新型图神经网络模型，直接处理车辆路径的图结构数据。
- 实验验证该模型在多个标准测试集上表现优于传统启发式算法。
- 适合对交通调度、物流规划感兴趣的工程师和研究者。
车辆路径问题旨在优化车辆路线以满足特定位置客户的需求。路径图包含多层级的配送中心和客户位置。多年来，已开发出多种优化方法，主要基于经典启发式算法：遗传算法、模拟退火、禁忌搜索、蚁群优化、萤火虫算法。近年来，机器学习的发展为复杂问题提供了新的工具集，即丰富的神经网络家族。神经网络的主要应用领域是分类和回归。路径优化可视为神经网络的新挑战。本文首先分析了神经网络工具的适用性，随后详细介绍了新型图神经网络模型。基于测试实验的效率分析表明，所提出的神经网络架构具有实际应用价值。
原文摘要 · Abstract (English)
The Vehicle Routing Problem is about optimizing the routes of vehicles to meet the needs of customers at specific locations. The route graph consists of depots on several levels and customer positions. Several optimization methods have been developed over the years, most of which are based on some type of classic heuristic: genetic algorithm, simulated annealing, tabu search, ant colony optimization, firefly algorithm. Recent developments in machine learning provide a new toolset, the rich family of neural networks, for tackling complex problems. The main area of application of neural networks is the area of classification and regression. Route optimization can be viewed as a new challenge for neural networks. The article first presents an analysis of the applicability of neural network tools, then a novel graphical neural network model is presented in detail. The efficiency analysis based on test experiments shows the applicability of the proposed NN architecture.
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