求解有界船队规模的CVRP的有监督置换不变网络
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size
摘要：
学习解决组合优化问题，如车辆路径问题，提供了很大的计算优势，经典的运筹学求解器和算法。最近开发的深度强化学习方法要么迭代地改进初始给定的解决方案，要么顺序地构建一组单独的旅行。然而，大多数现有的基于学习的方法是不能够工作的固定数量的车辆，从而绕过复杂的分配问题的客户到一个先验给定数量的可用车辆。另一方面，这使得它们不太适合实际应用，因为许多物流服务提供商依赖于为特定的有限车队规模提供的解决方案，并且无法适应车辆数量的短期变化。相比之下，我们提出了一个强大的监督深度学习框架，从头开始构建一个完整的旅游计划，同时尊重先验的固定数量的可用车辆。结合一个有效的后处理方案，我们的监督方法不仅更快，更容易训练，但也取得了有竞争力的结果，结合车辆成本的实际方面。在彻底的对照实验中，我们将我们的方法与多种最先进的方法进行了比较，在这些方法中，我们展示了稳定的性能，同时利用更少的车辆，并揭示了相关工作的实验方案中存在的不一致性。
英文摘要：
Learning to solve combinatorial optimization problems, such as the vehicle routing problem, offers great computational advantages over classical operations research solvers and heuristics. The recently developed deep reinforcement learning approaches either improve an initially given solution iteratively or sequentially construct a set of individual tours. However, most of the existing learning-based approaches are not able to work for a fixed number of vehicles and thus bypass the complex assignment problem of the customers onto an apriori given number of available vehicles. On the other hand, this makes them less suitable for real applications, as many logistic service providers rely on solutions provided for a specific bounded fleet size and cannot accommodate short term changes to the number of vehicles. In contrast we propose a powerful supervised deep learning framework that constructs a complete tour plan from scratch while respecting an apriori fixed number of available vehicles. In combination with an efficient post-processing scheme, our supervised approach is not only much faster and easier to train but also achieves competitive results that incorporate the practical aspect of vehicle costs. In thorough controlled experiments we compare our method to multiple state-of-the-art approaches where we demonstrate stable performance, while utilizing less vehicles and shed some light on existent inconsistencies in the experimentation protocols of the related work.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
