Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
12-2020
Abstract
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad of specialized knowledge and often delivering limited performance. In this paper, we propose to automatically learn PDRs via an end-to-end deep reinforcement learning agent. We exploit the disjunctive graph representation of JSSP, and propose a Graph Neural Network based scheme to embed the states encountered during solving. The resulting policy network is size-agnostic, effectively enabling generalization on large-scale instances. Experiments show that the agent can learn high-quality PDRs from scratch with elementary raw features, and demonstrates strong performance against the best existing PDRs. The learned policies also perform well on much larger instances that are unseen in training.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020 Dec 6-12
First Page
1621
Last Page
1632
Identifier
10.48550/arXiv.2010.12367
Publisher
Curran Associates Inc.
City or Country
New York
Citation
ZHANG, Cong; SONG, Wen; CAO, Zhiguang; ZHANG, Jie; TAN, Puay Siew; and CHI, Xu.
Learning to dispatch for job shop scheduling via deep reinforcement learning. (2020). Proceedings of the 34th Conference on Neural Information Processing Systems, Vancouver, Canada, 2020 Dec 6-12. 1621-1632.
Available at: https://ink.library.smu.edu.sg/sis_research/8133
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.48550/arXiv.2010.12367