中国机械工程 ›› 2023, Vol. 34 ›› Issue (15): 1832-1847.DOI: 10.3969/j.issn.1004-132X.2023.15.008

• 可持续制造 • 上一篇    下一篇

考虑设备预维护的特种车辆白车身试制车间绿色调度优化方法

李西兴1,2;周文龙1,2;唐红涛3;吴锐1,2   

  1. 1.湖北工业大学机械工程学院,武汉,430068
    2.湖北工业大学现代制造质量工程湖北省重点实验室,武汉,430068
    3.武汉理工大学机电工程学院,武汉,430070
  • 出版日期:2023-08-10 发布日期:2023-08-14
  • 通讯作者: 唐红涛(通信作者),男,1987年生,副教授、博士研究生导师。研究方向为智能优化算法及制造企业信息化应用等。E-mail:tanghongtaozc@163.com。
  • 作者简介:李西兴,男,1990年生,副教授、博士。研究方向为生产调度与优化、制造业信息化。E-mail:li_xi_xing@126.com。
  • 基金资助:
    国家自然科学基金(51805152,52075401);湖北省自然科学基金 (2022CFB445);湖北工业大学高层次人才科研基金(GCRC2020009);湖北工业大学绿色工业引领计划(XJ2021005001)

Green Scheduling Optimization Method of Special Vehicle Body-in-White Prototype Shops Considering Equipment Preventive Maintenance

LI Xixing1,2;ZHOU Wenlong1,2;TANG Hongtao3;WU Rui1,2   

  1. 1.School of Mechanical Engineering,Hubei University of Technology,Wuhan,430068
    2.Hubei Key Laboratory of Modern Manufacturing and Quality Engineering,Hubei University of 
    Technology,Wuhan,430068
    3.School of Mechanical and ElectronicEngineering,Wuhan University of Technology,Wuhan,430070
  • Online:2023-08-10 Published:2023-08-14

摘要: 综合考虑最长完工时间、设备总能耗以及总烟尘排放,构建了典型柔性作业车间多目标绿色调度优化模型,并设计了一种改进人工蜂群算法对其进行求解。首先,根据激光设备功率会发生周期性衰减的特点,提出了一种区分激光设备和普通机械设备的预维护策略,以减小最长完工时间并降低设备故障发生频率。其次,设计了一种基于设备分配和功率选择的变异方式以增强算法局部搜索能力,并在跟随蜂阶段引入基于拥挤距离的选择方法进行种群更新以获得优质个体。最后,扩展现有通用测试集并开展对比实验,同时以某汽车装备制造企业的特种车辆白车身试制车间为实例,进一步验证了模型与算法的有效性和可行性。

关键词: 柔性作业车间, 绿色调度, 设备预维护, 激光加工

Abstract: A typical multi-objective flexible job-shop green scheduling model was established, and the makespan, total energy consumption of equipment and total smoke emission were taken into consideration. And an improved artificial bee colony algorithm was designed to solve this model. Firstly, according to the characteristics of periodic power attenuation of laser equipment, a preventive maintenance strategy that could distinguish laser equipment from ordinary mechanical equipment was proposed to reduce the makespan and the frequency of equipment failure. Then, a mutation method was designed based on equipment allocation and power selection, which could improve the local search ability of the algorithm. A selection method was introduced based on crowded distance in the follow bee search stage for population regeneration to obtain high-quality individuals. Finally, the comparison experiments were carried out based on the expanded common benchmark. Meanwhile, the effectiveness and feasibility of the model and algorithm were verified through the production case of a special vehicle body-in-white prototype workshop in an automotive equipment manufacturing enterprise. 

Key words: flexible job shop, green scheduling, equipment preventive maintenance, laser processing

中图分类号: