现代制造工程 ›› 2024, Vol. 524 ›› Issue (5): 31-38.doi: 10.16731/j.cnki.1671-3133.2024.05.005

• 先进制造系统管理运作 • 上一篇    下一篇

基于遗传算法的家电智能生产线分布式资源调度算法设计*

张殷晨, 左鹏奇, 王逸飞, 林楠, 谢润, 金立军   

  1. 同济大学电子与信息工程学院,上海 201804
  • 收稿日期:2023-05-22 出版日期:2024-05-18 发布日期:2024-05-30
  • 通讯作者: 金立军,博士,教授,主要研究方向为智能制造、电磁场理论、电力设备故障诊断和高电压工程。E-mail:jinlijun@tongji.edu.cn
  • 作者简介:张殷晨,硕士研究生,主要研究方向为智能制造与数字孪生。E-mail:2230663@tongji.edu.cn;左鹏奇,博士研究生,主要研究方向为包括智能制造和数字孪生。E-mail:2111490@tongji.edu.cn;王逸飞,博士研究生,主要研究方向为智能制造、自然语言处理和机器视觉。E-mail:1552442@tongji.edu.cn;林楠,硕士研究生,主要研究方向为智能制造和数字孪生。E-mail:2130661@tongji.edu.cn;谢润,硕士研究生,主要研究方向为电力设备仿真与结构优化。E-mail:2130662@tongji.edu.cn
  • 基金资助:
    *国家重点研发计划项目(2019YFB1706700)

Design of distributed resource scheduling algorithm for household appliance intelligent production line based on genetic algorithm

ZHANG Yinchen, ZUO Pengqi, WANG Yifei, LIN Nan, XIE Run, JIN Lijun   

  1. School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
  • Received:2023-05-22 Online:2024-05-18 Published:2024-05-30

摘要: 目前家电智能生产线存在多任务操作冲突、调度控制响应时间长以及制造资源利用率低等问题。为了实现家电智能生产线的合理资源调度,基于多目标优化和分布式资源调度理论,建立了家电智能生产线分布式资源调度模型,提出了基于遗传算法的家电智能生产线分布式资源调度算法。仿真实验表明,与传统生产线调度方法相比,所提出的分布式资源调度算法最大完工时间缩短了5.76 %,解的适应度提高了8 %,验证了算法的可行性和高效性。

关键词: 分布式资源调度, 多目标优化, 柔性调度, 遗传算法

Abstract: At present, the intelligent production line of household appliances has many problems, such as multi-task operation conflict, long response time of scheduling control and low utilization rate of manufacturing resources. In order to realize the rational resource scheduling of household appliance intelligent production line, based on multi-objective optimization and distributed resource scheduling theory, a distributed resource scheduling model of household appliance intelligent production line was established, and a distributed resource scheduling algorithm based on genetic algorithm was proposed. The simulation results show that compared with the traditional production line scheduling method, the maximum completion time of the proposed distributed resource scheduling algorithm is reduced by 5.76 %, and the fitness of the solution is increased by 8 %, which verifies the feasibility and efficiency of the algorithm.

Key words: distributed resource scheduling, multi-objective optimization, flexible scheduling, genetic algorithm

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