论文标题
一个深度学习的注意力模型，以解决车辆路线问题以及随时间窗口的接送和交付问题
A deep learning Attention model to solve the Vehicle Routing Problem and the Pick-up and Delivery Problem with Time Windows
论文作者
论文摘要
法国公共火车公司SNCF正在尝试通过解决车辆路线问题来开发新型的运输服务。尽管许多深度学习模型已被用来解决有效的车辆路由问题，但很难考虑与时间相关的约束。在本文中，我们通过时间窗口（CVRPTW）解决了电容的车辆路由问题，并使用时间窗口（CPDPTW）的电容拾取和交付问题，并具有建设性的迭代深度学习算法。我们使用注重编码器解码器结构，并设计一种新颖的插入启发式，以检查CPDPTW。我们的模型产生的结果比CVRPTW上最知名的学习解决方案更好。我们展示了深度学习技术解决CPDPTW的可行性，但在计算复杂性方面见证了我们迭代方法的局限性。
SNCF, the French public train company, is experimenting to develop new types of transportation services by tackling vehicle routing problems. While many deep learning models have been used to tackle efficiently vehicle routing problems, it is difficult to take into account time related constraints. In this paper, we solve the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and the Capacitated Pickup and Delivery Problem with Time Windows (CPDPTW) with a constructive iterative Deep Learning algorithm. We use an Attention Encoder-Decoder structure and design a novel insertion heuristic for the feasibility check of the CPDPTW. Our models yields results that are better than best known learning solutions on the CVRPTW. We show the feasibility of deep learning techniques for solving the CPDPTW but witness the limitations of our iterative approach in terms of computational complexity.
