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.
@article{arxiv.2204.11399,
title = {Efficient Neural Neighborhood Search for Pickup and Delivery Problems},
author = {Yining Ma and Jingwen Li and Zhiguang Cao and Wen Song and Hongliang Guo and Yuejiao Gong and Yeow Meng Chee},
journal= {arXiv preprint arXiv:2204.11399},
year = {2022}
}