论文标题
部分可观测时空混沌系统的无模型预测
Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem
论文作者
论文摘要
电容的车辆路由问题是一个众所周知的NP硬化问题，它构成了寻找将产品运送到多个位置的最佳路线的挑战。最近，已经出现了新的努力来创建建设性和扰动的启发式方法，以使用深度学习来解决这个问题。在本文中，我们加入了共同的深层构造函数和扰动者的努力，该构造方法和扰动器结合了两个强大的建设性和扰动性深度学习的启发式方法，并使用其核心的注意机制结合了。此外，我们通过提出一种记忆有效的算法来改善电容车辆路由问题的注意力模型，该算法将其记忆复杂性降低了节点数量的因素。我们的方法显示出令人鼓舞的结果。与其他多种深度学习方法相比，它表明了普通数据集的成本提高。它还取得了从运营研究领域的最先进的启发式方法。此外，针对注意模型模型模型的提出的记忆有效算法可以在超过100个节点的问题实例中使用。
The Capacitated Vehicle Routing Problem is a well-known NP-hard problem that poses the challenge of finding the optimal route of a vehicle delivering products to multiple locations. Recently, new efforts have emerged to create constructive and perturbative heuristics to tackle this problem using Deep Learning. In this paper, we join these efforts to develop the Combined Deep Constructor and Perturbator, which combines two powerful constructive and perturbative Deep Learning-based heuristics, using attention mechanisms at their core. Furthermore, we improve the Attention Model-Dynamic for the Capacitated Vehicle Routing Problem by proposing a memory-efficient algorithm that reduces its memory complexity by a factor of the number of nodes. Our method shows promising results. It demonstrates a cost improvement in common datasets when compared against other multiple Deep Learning methods. It also obtains close results to the state-of-the art heuristics from the Operations Research field. Additionally, the proposed memory efficient algorithm for the Attention Model-Dynamic model enables its use in problem instances with more than 100 nodes.
