Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.24963/ijcai.2022/662 | International Joint Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Search: Search and Machine Learning,Machine Learning: Deep Reinforcement Learning,Search: Local search,Multidisciplinary Topics and Applications: Transportation | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi-Ning Ma | 1 | 8 | 2.49 |
| Jingwen Li | 2 | 33 | 12.58 |
| Zhiguang Cao | 3 | 0 | 0.68 |
| Wen Song | 4 | 4 | 5.13 |
| Hongliang Guo | 5 | 0 | 0.34 |
| Yuejiao Gong | 6 | 0 | 0.68 |
| Yeow Meng Chee | 7 | 0 | 0.68 |