Complex real-life routing challenges can be modeled as variations of well-known combinatorial optimization problems. These routing problems have long been studied and are difficult to solve at scale. The particular setting may also make exact formulation difficult. Deep Learning offers an increasingly attractive alternative to traditional solutions, which mainly revolve around the use of various heuristics. Deep Learning may provide solutions which are less time-consuming and of higher quality at large scales, as it generally does not need to generate solutions in an iterative manner, and Deep Learning models have shown a surprising capacity for solving complex tasks in recent years. Here we consider a particular variation of the Capacitated Vehicle Routing (CVRP) problem and investigate the use of Deep Learning models with explicit memory components. Such memory components may help in gaining insight into the model's decisions as the memory and operations on it can be directly inspected at any time, and may assist in scaling the method to such a size that it becomes viable for industry settings.

中文翻译：

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具有记忆增强神经网络的复杂车辆路线

复杂的现实路由挑战可以建模为众所周知的组合优化问题的变体。这些路由问题长期以来一直被研究并且难以大规模解决。特定的设置也可能使精确的公式化变得困难。深度学习为传统解决方案提供了越来越有吸引力的替代方案，传统解决方案主要围绕使用各种启发式方法。深度学习可以大规模提供耗时更少、质量更高的解决方案，因为它通常不需要以迭代方式生成解决方案，而且近年来深度学习模型在解决复杂任务方面表现出惊人的能力。在这里，我们考虑了 Capacitated Vehicle Routing (CVRP) 问题的一个特定变体，并研究了具有显式内存组件的深度学习模型的使用。此类内存组件可能有助于深入了解模型的决策，因为可以随时直接检查内存及其上的操作，并且可能有助于将方法扩展到适合行业设置的大小。