Prompt Learning for Generalized Vehicle Routing
Authors: Fei Liu, Xi Lin, Weiduo Liao, Zhenkun Wang, Qingfu Zhang, Xialiang Tong, Mingxuan Yuan
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed prompt learning approach facilitates the fast adaptation of pre-trained routing models. It also outperforms existing generalized models on both in-distribution prediction and zero-shot generalization to a diverse set of new tasks. |
| Researcher Affiliation | Collaboration | Fei Liu1, Xi Lin1, Weiduo Liao1, Zhenkun Wang2, , Qingfu Zhang1, , Xialiang Tong3, and Mingxuan Yuan3 1City University of Hong Kong 2Southern University of Science and Technology 3Huawei Noah s Ark Lab |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code implementation is available online at https://github.com/Fei Liu36/Prompt VRP. |
| Open Datasets | Yes | The detailed instance generation is introduced in the Appendix, which is the same as that used by Zhou [2023]. We adopt the distribution proposed by Bi [2022], which consists of a total of 12 different distributions, including cluster (CL), expansion (EA), explosion (EO), implosion (IM), grid (GR), and mixed (MX). More experiments on real-world instances are conducted on five test suites: set A, B, P, X [Uchoa et al., 2017], and XML [Queiroga et al., 2021] from CVRPLIB 1. 1http://vrp.atd-lab.inf.puc-rio.br/ |
| Dataset Splits | No | The paper describes the instance generation and uses training distributions and test distributions, but it does not explicitly specify a separate 'validation' dataset split with percentages or counts. |
| Hardware Specification | Yes | It takes only about 2.5 days on a single RTX 2080Ti with 11 GB GPU memory. |
| Software Dependencies | No | The paper mentions algorithms and pre-trained models used (e.g., REINFORCE, Attention model, POMO), but does not specify software packages or libraries with version numbers (e.g., Python, PyTorch, TensorFlow, or specific library versions). |
| Experiment Setup | Yes | The prompt size is set to be M = 16, and the number of prompted tokens for each layer is D = 5. As there are L = 6 MHA layers in the encoder and the embedding size for each token is E = 128, the lengths of the key and prompt vectors are 6 128 = 768 and 5 6 128 = 3840, respectively. The prompts are trained with a batch size of B = 64. The maximum number of epochs is 10, 000 and there are 1, 000 instances for each epoch. The learning rate decays from 1e 3 to 1e 5. |