陆耀珣, 王立平, 孙丽荣, 杨金光, 王冬, 李学崑. 基于改进遗传算法的轧辊磨削产线智能排程方法[J]. 制造技术与机床, 2022, (8): 149-155. DOI: 10.19287/j.mtmt.1005-2402.2022.08.023
引用本文: 陆耀珣, 王立平, 孙丽荣, 杨金光, 王冬, 李学崑. 基于改进遗传算法的轧辊磨削产线智能排程方法[J]. 制造技术与机床, 2022, (8): 149-155. DOI: 10.19287/j.mtmt.1005-2402.2022.08.023
LU Yaoxun, WANG Liping, SUN Lirong, YANG Jinguang, WANG Dong, LI Xuekun. Intelligent scheduling method of rollergrindingproduction line based on improved genetic algorithm[J]. Manufacturing Technology & Machine Tool, 2022, (8): 149-155. DOI: 10.19287/j.mtmt.1005-2402.2022.08.023
Citation: LU Yaoxun, WANG Liping, SUN Lirong, YANG Jinguang, WANG Dong, LI Xuekun. Intelligent scheduling method of rollergrindingproduction line based on improved genetic algorithm[J]. Manufacturing Technology & Machine Tool, 2022, (8): 149-155. DOI: 10.19287/j.mtmt.1005-2402.2022.08.023

基于改进遗传算法的轧辊磨削产线智能排程方法

Intelligent scheduling method of rollergrindingproduction line based on improved genetic algorithm

  • 摘要: 针对多工件、多工序、多磨床和多天车的轧辊磨削产线调度问题,提出一种改进遗传算法实现“工件-设备-天车”时空耦合约束下的高效智能排程。建立轧辊磨削产线调度模型,从编码、选择、交叉及变异等4个方面对遗传算法进行改进,在生成可行解的同时提高寻优及跳出局部最优的能力;设计基于动作分解的天车解码规则及系列算子,完成产线智能排程。以最大完工时间为性能指标并与其他算法对比,结果表明所提出的智能排程方法明显更优。

     

    Abstract: Aiming at the scheduling problem of roller grinding production line with multiple workpieces, multiple processes, multiple grinding machines and multiple cranes, an improved genetic algorithm is proposed to realize efficient and intelligent scheduling under the constraints of “workpiece-equipment-crane” space-time coupling. The scheduling model of the roller grinding production line is established, and the genetic algorithm is improved from four aspects of coding, selection, crossover, and mutation, so as to generate feasible solutions and improve the ability to seek optimization and jump out of the local optimum.Crane decoding rules and series operators based on action decomposition are further designed to complete intelligent scheduling. Taking the maximum completion time as the performance index and comparing with other algorithms, the results show that the proposed intelligent scheduling method is obviously better.

     

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