Li Hong Ming
ABSTRACT
Clarify the relationship between wind tunnel test and power resource guarantee, use the existing information system resources, construct the test scheduling system framework, guide and promote the wind tunnel test scheduling to the direction of integration and intelligence development. An MDP wind tunnel test scheduling method is proposed to explore the application of artificial intelligence in the area of the wind tunnel test scheduling, which lay a theoretical and technical foundation for opening up the channel of the multi-source heterogeneous information system related to the wind tunnel test scheduling next, and forming the close relation between the situation awareness and prediction system of the wind tunnel test scheduling.
- H. Aytug and S. Bhattacharyya. 1994. A Review of Machine Learning in Scheduling. IEEE Transactions on Engineering Management 41, 2 (1994), 165–171.Google Scholar
Cross Ref
- G. Bel, Bensana E., Dubois D., Erschler J., and Esquirol P.1989. A knowledge-based approach to industrial job-shop scheduling. A. Kusiak, ed. Taylor and Francis 15, 1 (1989), 207–246.Google Scholar
- Ammons Jane C., T. Govindaraj, and Christine M. Mitchell. 1998. A supervisory control paradigm for real-time control of flexible manufacturing systems. Annals of Operations Research 15, 1 (1998), 313–335.Google Scholar
- Jonathan H. Connell and Sridhar Mahadevan K.B. 1999. Reinforcement learning: An introduction. Robotica 117, 2 (1999), 229–235.Google Scholar
- Blackstone J. H., Phillips D.T., and Hogg G. L. 1982. The state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res 120, 1 (1982), 27–45.Google Scholar
- Y. Ji, J. Wang, J. Xu, and D. Li. 2021. Data-Driven Online Energy Scheduling of a Microgrid Based on Deep Reinforcement Learning. Energies 14, 8 (2021), 2120.Google ScholarCross Ref
- Jackson J.R. 2004. Jobshop-Like Queueing Systems. Management Science 50(2004), 1796–1802.Google ScholarDigital Library
- M. Liu. 2009. A Survey of Data-based Production Scheduling Methods. ACTA AUTOMATICA SINICA 35, 6 (2009), 785–806.Google ScholarCross Ref
- XiaoPing Liu and Lin Du. 2012. Improvement of task scheduling based on Q-learning. Journal of Graphics 133, 3 (2012), 11–16.Google Scholar
- ChangJun Luo and Juan Zheng. 2021. Integrated Support Architecture for Wind Tunnel Group Power System Based on CPS. Measurement and Control Technology 140, 7 (2021), 100–106.Google Scholar
- A.I. Pop F. Orhean and Raicu I. 2018. New scheduling approach using reinforcement learning for heterogeneous distributed systems. J. Parallel and Distrib. Comput. 117 (2018), 292–302.Google ScholarDigital Library
- K. Ramamritham and J. A. Stankovic. 1994. Scheduling algorithms and operating systems support for real-time systems. Proc. IEEE 82, 1 (1994), P.55–67.Google ScholarCross Ref
- K. Ramamritham and J. A. Stankovic. 1994. Scheduling algorithms and operating systems support for real-time systems. Proc. IEEE 82, 1 (1994), P.55–67.Google ScholarCross Ref
Index Terms
- Design and Method Research of Wind Tunnel Test Scheduling System for Multi-source Heterogeneous Data
Recommendations
Research on Test Aircraft Quantitative Analysis Model during Flight Test Planning
ISCSIC 2020: Proceedings of the 2020 4th International Symposium on Computer Science and Intelligent ControlThe process of civil aircraft development is a complex system, where flight test is an extremely important process of the project. In the top-level planning of flight test, it is particularly important to determine the quantity of test aircraft, which ...
Comments