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Design of integrated steel production scheduling knowledge network system

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Abstract

The knowledge network system was developed based on the needs of the modern iron and steel enterprise integrated production management. System was mainly composed of knowledge base, model base and algorithm library .The core part of system was knowledge and the key knowledge represent method was hybrid knowledge expression. Herein, model knowledge representation and intelligent matching mechanism was proposed. The scheduling results were displayed by Gantt chart through calling corresponding intelligent optimization algorithm with automatically selecting the model. The system solved the problem of process, “non-synchronous” at casting and rolling and enhanced the ability of dynamic scheduling. It achieved the iron and steel intelligent production scheduling and guided the real production effectively, reduced the operation of decision maker, also improved the enterprises’ market competitiveness and capacity to face the disturbance. Finally, the system was verified effective by the simulation examples.

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Acknowledgements

This work was supported by Grants of National Natural Science Foundation of China (Grant No. 71271160).

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Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan, People’s Republic of China

    Le Yang, Guozhang Jiang, Xi Chen, Gongfa Li, Tingting Li & Xiaowu Chen

  2. Continental; Automotive Corporation (LYG) Co. Ltd, Lianyungang, People’s Republic of China

    Le Yang

  3. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, People’s Republic of China

    Guozhang Jiang, Gongfa Li, Tingting Li & Xiaowu Chen

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  1. Le Yang
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  2. Guozhang Jiang
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  3. Xi Chen
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  4. Gongfa Li
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  5. Tingting Li
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  6. Xiaowu Chen
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Correspondence to Xi Chen.

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Yang, L., Jiang, G., Chen, X. et al. Design of integrated steel production scheduling knowledge network system. Cluster Comput 22 (Suppl 4), 10197–10206 (2019). https://doi.org/10.1007/s10586-017-1215-7

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