Efficient Neural Neighborhood Search for Pickup and Delivery Problems
Abstract
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.
Cite
Text
Ma et al. "Efficient Neural Neighborhood Search for Pickup and Delivery Problems." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/662Markdown
[Ma et al. "Efficient Neural Neighborhood Search for Pickup and Delivery Problems." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/ma2022ijcai-efficient/) doi:10.24963/IJCAI.2022/662BibTeX
@inproceedings{ma2022ijcai-efficient,
title = {{Efficient Neural Neighborhood Search for Pickup and Delivery Problems}},
author = {Ma, Yining and Li, Jingwen and Cao, Zhiguang and Song, Wen and Guo, Hongliang and Gong, Yuejiao and Chee, Yeow Meng},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {4776-4784},
doi = {10.24963/IJCAI.2022/662},
url = {https://mlanthology.org/ijcai/2022/ma2022ijcai-efficient/}
}