The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A reinforcement learning approach to parameter estimation in dynamic job shop scheduling
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Cites
Mladenović, N., & Hansen, P. (1997) · Computers & Operations Research · doi:10.1016/s0305-0548(97)00031-2 · Cited by 4,254
The Shifting Bottleneck Procedure for Job Shop Scheduling
Adams, J., Balas, E., & Zawack, D. J. (1988) · Management Science · doi:10.1287/mnsc.34.3.391 · Cited by 1,603
The job shop scheduling problem: Conventional and new solution techniques
Błażewicz, J., Domschke, W., & Pesch, E. (1996) · European Journal of Operational Research · doi:10.1016/0377-2217(95)00362-2 · Cited by 536
Dynamic job-shop scheduling using reinforcement learning agents
Aydin, M. E., & Öztemel, E. (2000) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(00)00087-7 · Cited by 274
Application of reinforcement learning for agent-based production scheduling
Wang, Y.-C., & Usher, J. M. (2005) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2004.08.018 · Cited by 233
Automatic Programming via Iterated Local Search for Dynamic Job Shop Scheduling
Nguyen, S., Zhang, M., Johnston, M., & Tan, K. C. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2317488 · Cited by 160
Vinod, V., & Sridharan, R. (2010) · International Journal of Production Economics · doi:10.1016/j.ijpe.2010.08.017 · Cited by 143
Variable neighborhood tabu search and its application to the median cycle problem
Pérez, J. A. M., Moreno-Vega, J. M., & Rodríguez‐Martín, I. (2003) · European Journal of Operational Research · doi:10.1016/s0377-2217(02)00831-7 · Cited by 88
Park, C. H., Mei, Y., Nguyen, S., Chen, G., Johnston, M., & Zhang, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30668-1_8 · Cited by 31
Chen, X., Hao, X., Lin, H. W., et al. (2010) · IEEE International Conference on Automation and Logistics (ICAL) · doi:10.1109/ical.2010.5585316 · Cited by 28
Cited by
Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook
Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4047855 · Cited by 584
Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning
Luo, S. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106208 · Cited by 579
Chen, R., Yang, B., Li, S., & Wang, S. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106778 · Cited by 548
A review of machine learning for the optimization of production processes
Weichert, D., Link, P., Stoll, A., Rüping, S., Ihlenfeldt, S., & Wrobel, S. (2019) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-019-03988-5 · Cited by 363
Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2020.1747642 · Cited by 309
Metadata
- Authors
- Shahrabi, J., Adibi, M. A., & Mahootchi, M.
- Venue
- Computers & Industrial Engineering
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 110
- Pages
- 75-82
- ISSN
- 0360-8352 (ISSN-L)
- OpenAlex
- W2618749399
- Cited by
- 313
- References
- 11
- FWCI
- 17.39
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017). A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2017.05.026
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
