The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Dynamic job-shop scheduling using reinforcement learning agents
Open accessAbstract
Static and dynamic scheduling methods have attracted a lot of attention in recent years. Among these, dynamic scheduling techniques handle scheduling problems where the scheduler does not possess detailed information about the jobs, which may arrive at the shop at any time. In this paper, an intelligent agent based dynamic scheduling system is proposed. It consists of two independent components: the agent and the simulated environment. The agent selects the most appropriate priority rule according to the shop conditions in real time, while simulated environment performs scheduling activities using the rule selected by the agent. The agent is trained by an improved reinforcement learning algorithm through the learning stage and then it successively makes decisions to schedule the operations.
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Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114820 · Cited by 553
A reinforcement learning approach to parameter estimation in dynamic job shop scheduling
Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.05.026 · Cited by 313
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems
Liu, C.-L., Chang, C.-C., & Tseng, C.-J. (2020) · IEEE Access · doi:10.1109/access.2020.2987820 · Cited by 281
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Metadata
- Authors
- Aydin, M. E., & Öztemel, E.
- Venue
- Robotics and Autonomous Systems
- Publisher
- Elsevier BV
- Published
- 2000-11
- Volume
- 33
- Issue
- 2-3
- Pages
- 169-178
- ISSN
- 0921-8890 (ISSN-L)
- OpenAlex
- W2028145591
- Cited by
- 274
- References
- 25
- FWCI
- 16.54
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
