LaDe: The First Comprehensive Last-mile Express Dataset from Industry-Reference-Cited by-同舟云学术

LaDe: The First Comprehensive Last-mile Express Dataset from Industry

Published:2024-08-24 Issue: Volume: Page:5991-6002
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wu Lixia¹^ORCID,Wen Haomin²^ORCID,Hu Haoyuan¹^ORCID,Mao Xiaowei²^ORCID,Xia Yutong³^ORCID,Shan Ergang¹^ORCID,Zheng Jianbin¹^ORCID,Lou Junhong¹^ORCID,Liang Yuxuan⁴^ORCID,Yang Liuqing⁴^ORCID,Zimmermann Roger³^ORCID,Lin Youfang⁵^ORCID,Wan Huaiyu⁵^ORCID

Affiliation:

1. Cainiao Network, Hangzhou, China

2. School of Computer and Information Technology, Beijing Jiaotong University & Cainiao Network, Beijing, China

3. National University of Singapore, Singapore, Singapore

4. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

5. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

Funder

Guangzhou-HKUST(GZ) Joint Funding Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671548

Reference62 articles.

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3. Irwan Bello Hieu Pham Quoc V Le Mohammad Norouzi and Samy Bengio. 2017. Neural combinatorial optimization with reinforcement learning. In ICLR.

4. Rishi Bommasani Drew A Hudson Ehsan Adeli Russ Altman Simran Arora Sydney von Arx Michael S Bernstein Jeannette Bohg Antoine Bosselut Emma Brunskill et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).

5. Last-mile delivery concepts: a survey from an operational research perspective