Deep Reinforcement Learning as a Job Shop Scheduling Solver: A Literature Review-Reference-Cited by-同舟云学术

Deep Reinforcement Learning as a Job Shop Scheduling Solver: A Literature Review

Published:2019-03-21 Issue: Volume: Page:350-359
ISSN:2194-5357
Container-title:Hybrid Intelligent Systems
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Author:

Cunha Bruno,Madureira Ana M.,Fonseca Benjamim,Coelho Duarte

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-14347-3_34

Reference31 articles.

1. Casavant, T.L., Kuhl, J.G.: A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Trans. Softw. Eng. 14(2), 141–154 (1988)

2. Ahire, S., Greenwood, G., et al.: Workforce-constrained preventive maintenance scheduling using evolution strategies. Decis. Sci. 31(4), 833–859 (2000)

3. Sonmez, A.I., Baykasoglu, A.: A new dynamic programming formulation of (n x m) flowshop sequencing problems with due dates. Int. J. Prod. Res. 36(8), 2269–2283 (1998)

4. Yamada, T., Yamada, T., Nakano, R.: Genetic algorithms for job-shop scheduling problems. In: Modern Heuristic for Decision Support, pp. 474–479 (1997)

5. McKay, K.N., Safayeni, F.R., Buzacott, J.A.: Job-shop scheduling theory: what is relevant? Interfaces 18(4), 84–90 (1988)

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