论文标题
有效的神经邻居寻找接送和交货问题
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
论文作者
论文摘要
我们提出了一种有效的神经邻域搜索（N2S），以解决取货和交付问题（PDPS）。具体而言，我们设计了强大的综合注意力，使香草自我注意力综合了有关路线解决方案的各种特征。我们还利用了两个自定义的解码器，它们会自动学习执行拾取节点对的删除和重新插入以应对优先限制。此外，利用多样性增强方案以进一步改善性能。我们的N2是通用的，并且对两个规范PDP变体进行了广泛的实验表明，它可以在现有神经方法之间产生最新的结果。此外，它甚至超过了众所周知的LKH3求解器在更受限的PDP变体上。我们针对N2S的实施可在线获得。
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.
