The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Real-time scheduling for a smart factory using a reinforcement learning approach
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Learning Deep Architectures for AI
Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050
Data Mining: Concepts and Techniques
Thamaraiselvi, G., & Kaliammal, A. (2014) · Journal of Information and Knowledge · Cited by 4,088
Cyber Physical Systems: Design Challenges
Lee, E. A. (2008) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc.2008.25 · Cited by 3,638
Clustering of the self-organizing map
Vesanto, J., & Alhoniemi, E. J. (2000) · IEEE Transactions on Neural Networks · doi:10.1109/72.846731 · Cited by 2,692
Feature Selection for Knowledge Discovery and Data Mining
Liu, H., & Motoda, H. (1998) · Springer eBooks · doi:10.1007/978-1-4615-5689-3 · Cited by 2,216
Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment
Lee, J., Kao, H.-A., & Yang, S. (2014) · Procedia CIRP · doi:10.1016/j.procir.2014.02.001 · Cited by 1,965
Implementing Smart Factory of Industrie 4.0: An Outlook
Wang, S., Wan, J., Li, D., & Zhang, C. (2016) · International Journal of Distributed Sensor Networks · doi:10.1155/2016/3159805 · Cited by 1,527
Recent advances and trends in predictive manufacturing systems in big data environment
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-A. (2013) · Manufacturing Letters · doi:10.1016/j.mfglet.2013.09.005 · Cited by 1,090
A state-of-the-art survey of dispatching rules for manufacturing job shop operations
Blackstone, J. H., Phillips, D. T., & Hogg, G. L. (1982) · International Journal of Production Research · doi:10.1080/00207548208947745 · Cited by 1,018
A probabilistic approach to feature selection - a filter solution
Liu, H., & Setiono, R. (1996) · Cited by 656
Sequencing Rules and Due-Date Assignments in a Job Shop
Baker, K. R. (1984) · Management Science · doi:10.1287/mnsc.30.9.1093 · Cited by 496
Wolf, W. H. (2009) · Computer · doi:10.1109/mc.2009.81 · Cited by 377
Learning Internal Representations by Error Propagation, Parallel Distributed Processing
Rumelhart, D. E. (1986) · Medical Entomology and Zoology · Cited by 374
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 319
Wu, S.-Y. D., & Wysk, R. A. (1989) · International Journal of Production Research · doi:10.1080/00207548908942642 · Cited by 244
Analysis of scheduling rules for an FMS
Montazeri, M., & Wassenhove, L. N. V. (1990) · International Journal of Production Research · doi:10.1080/00207549008942754 · Cited by 239
Application of reinforcement learning for agent-based production scheduling
Wang, Y.-C., & Usher, J. M. (2004) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2004.08.018 · Cited by 233
Ye, N. (2013) · CRC Press eBooks · doi:10.1201/b15288-10 · Cited by 140
A study of scheduling rules of flexible manufacturing systems: A simulation approach
Sabuncuoğlu, İ. (1998) · International Journal of Production Research · doi:10.1080/002075498193877 · Cited by 133
Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach
Park, S. C., Raman, N., & Shaw, M. J. (1997) · IEEE Transactions on Robotics and Automation · doi:10.1109/70.611301 · Cited by 113
Dynamic scheduling of manufacturing systems using machine learning: An updated review
Priore, P., Gómez, A. G., Díez, R. P., & Rosillo, R. (2014) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060413000516 · Cited by 102
Learning effective new single machine dispatching rules from optimal scheduling data
Ólafsson, S., & Li, X. (2010) · International Journal of Production Economics · doi:10.1016/j.ijpe.2010.06.004 · Cited by 83
Shiue, Y.-R. (2008) · International Journal of Production Research · doi:10.1080/00207540701846236 · Cited by 82
Self-citationIntegration of inductive learning and neural networks for multi-objective FMS scheduling
Kim, C.-O., Min, H.-S., & Yih, Y. (1998) · International Journal of Production Research · doi:10.1080/002075498192652 · Cited by 77
Metan, G., Sabuncuoğlu, İ., & Pierreval, H. (2010) · International Journal of Production Research · doi:10.1080/00207540903307581 · Cited by 74
A mixed dispatching rule approach in FMS scheduling
Ishii, N., & Talavage, J. J. (1994) · International Journal of Flexible Manufacturing Systems · doi:10.1007/bf01324876 · Cited by 52
Su, C., & Shiue, Y.-R. (2003) · International Journal of Production Research · doi:10.1080/0020754031000090612 · Cited by 42
Self-citationOperating an FMC by a decision-tree-based adaptive production control system
Arzi, Y., & Iaroslavitz, L. (2000) · International Journal of Production Research · doi:10.1080/002075400189365 · Cited by 39
A multi-pass simulation-based, real-time scheduling and shop floor control system
Son, Y., Rodríguez-Rivera, H., & Wysk, R. A. (1999) · Transactions of The Society for Computer Simulation International · doi:10.5555/340538.340544 · Cited by 37
Auto-bias selection for developing learning-based scheduling systems
Chen, C.-C., Yih, Y., & Wu, Y. (1999) · International Journal of Production Research · doi:10.1080/002075499190879 · Cited by 32
The study of real time scheduling by an intelligent multi-controller approach
Guh, R., Shiue, Y.-R., & Tseng, T.-Y. (2010) · International Journal of Production Research · doi:10.1080/00207541003794884 · Cited by 29
Self-citationGoryachev, A., Kozhevnikov, S., Kolbova, E., Kuznetsov, O., Simonova, E., Skobelev, P. O., Tsarev, A., & Юрьевич, Ш. Я. (2012) · Advanced materials research · doi:10.4028/www.scientific.net/amr.630.508 · Cited by 29
Shiue, Y.-R., Guh, R., & Lee, K. (2012) · International Journal of Production Research · doi:10.1080/00207543.2011.636389 · Cited by 28
Self-citationKück, M., Ehm, J., Freitag, M., Frazzon, E. M., & Pimentel, R. (2016) · Advanced materials research · doi:10.4028/www.scientific.net/amr.1140.449 · Cited by 22
Shiue, Y.-R., Guh, R., & Tseng, T.-Y. (2012) · Computers & Industrial Engineering · doi:10.1016/j.cie.2012.01.004 · Cited by 18
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Herrmann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios. In: 49th Hawaii international conference on system sciences, Koloa, HI, USA (pp. 3928–3937).
Cited by
Sorted by citations
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
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 593
Cadavid, J. P. U., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01531-7 · Cited by 420
Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network
Lin, C., Deng, D., Chih, Y.-L., & Chiu, H.-T. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2908210 · Cited by 352
Firdose, S., Avgeris, M., Spatharakis, D., Santi, N., Dechouniotis, D., Violos, J., Leivadeas, A., Αθανασόπουλος, Ν., Mitton, N., & Papavassiliou, S. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108177 · Cited by 283
Metadata
- Authors
- Shiue, Y.-R., Lee, K.-C., & Su, C.-T.
- Affiliations
- Fujian Business University (CN)
- National Tsing Hua University (TW)
- Corresponding author
- Su, Chao-Ton
- Venue
- Computers & Industrial Engineering
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 125
- Pages
- 604-614
- ISSN
- 0360-8352 (ISSN-L)
- OpenAlex
- W2793262237
- Cited by
- 225
- References
- 44
- Citations by year
- 2026: 13 · 2025: 27 · 2024: 29 · 2023: 38 · 2022: 37 · 2021: 40 · 2020: 32 · 2019: 9
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Industrial Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Scheduling and Optimization Algorithms
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Advanced Manufacturing and Logistics Optimization
- Physical Sciences › Computer Science › Hardware and Architecture › Real-Time Systems Scheduling
- Keywords
- real-time scheduling, smart factory, reinforcement learning, Q-learning, shop floor control, knowledge base updating, offline learning, production performance, manufacturing systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review