Title: Intelligent optimisation algorithm of rolling schedule for steel integrated production

Authors: Le Yang; Guozhang Jiang; Xiaowu Chen; Gongfa Li

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China

Abstract: The paper presents an improved algorithm-heuristic genetic algorithm which is different from the traditional algorithm, and combining it with the mode knowledge in the knowledge base, we developed algorithm library that oriented steel production order. The algorithm, which has quick convergence speed and strong global search ability, avoids the defects of traditional genetic algorithm premature convergence. Firstly, we analyse the integrated steel production planning system structure, describe the rolling plan problem, give rolling planning model that considers maximum rolling miles, and give the detailed steps of the various specific rolling schedule algorithm, including the selection of clustering operator, construct initial solution, crossover operator and mutation operator and ranking operator, and then give the process of rolling planning algorithm. Finally, the algorithm is verified by the simulation examples, and the optimal solution is obtained, which not only improves the efficiency of the calculation, but also increases the reliability of the data.

Keywords: optimal production plan; intelligent scheduling; scheduling algorithm; heuristic genetic algorithm.

DOI: 10.1504/IJWMC.2019.100068

International Journal of Wireless and Mobile Computing, 2019 Vol.16 No.4, pp.364 - 368

Available online: 30 May 2019 *

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