Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization-Reference-Cited by-同舟云学术

Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization

Published:2024-08-24 Issue: Volume:32 Page:1898-1908
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Liu Fei¹^ORCID,Lin Xi¹^ORCID,Wang Zhenkun²^ORCID,Zhang Qingfu¹^ORCID,Xialiang Tong³^ORCID,Yuan Mingxuan⁴^ORCID

Affiliation:

1. City University of Hong Kong, Hong Kong, China

2. Southern University of Science and Technology, Shenzhen, China

3. Huawei Technologies Ltd., Shenzhen, China

4. Huawei Technologies Ltd., Hong Kong, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

National Natural Science Foundation of Shenzhen

Research Grants Council of the Hong Kong Special Administrative Region, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672040

Reference53 articles.

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4. Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, and Yeow Meng Chee. 2022. Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation. arXiv preprint arXiv:2210.07686 (2022).

5. Kris Braekers, Katrien Ramaekers, and Inneke Van Nieuwenhuyse. 2016. The vehicle routing problem: State of the art classification and review. Computers & industrial engineering, Vol. 99 (2016), 300--313.