具有设备动态性和加工速度选择的生产与维护集成调度研究

doi:10.3901/JME.2025.14.362

摘要/Abstract

摘要： 针对动态加工环境，现有文献对动态干扰事件的研究主要聚焦在新工件到达和设备随机故障，很少考虑设备数量动态变化(如增加和减少)的影响。为弥补这一不足，研究了一种包含新机器插入、旧机器报废、加工速度选择和预测性维护(Predictive maintenance, PdM)的多目标柔性作业车间生产与维护集成调度问题。具体研究内容包括：① 基于加速退化的Gamma退化过程，设计包含加工速度选择、可变PdM阈值和16种离散检测策略的多阶段-多阈值PdM策略；② 针对新机器插入、旧机器报废和生产调度问题规模，构建自适应重调度策略；③ 为求解该集成优化问题，提出自适应双种群协作多目标进化算法(Adaptive bi-population cooperative multi-objective evolutionary algorithm , ABCMOEA)。在数值仿真中，首先研究参数设置对ABCMOEA算法的影响；然后通过文献对比验证了协同初始化方法和三种局部搜索机制的有效性；紧接着通过与其他智能算法、PdM策略和重调度策略进行对比，分别验证ABCMOEA算法、PdM策略和ARS策略的优越性；最后通过敏感性分析发现，加工速度和维护水平的可选择范围对集成优化研究具有显著性影响，且前者的影响更大。

关键词: 柔性作业车间调度, 新机器插入, 旧机器报废, 加工速度选择, 维护水平选择, 预测性维护, 多目标进化算法

Abstract: For the dynamic processing environment, the existing research on dynamic disturbance events mainly focuses on new job arrival and machine random failure, and rarely considers the influence of dynamic changes in the total number of machines (such as increasing and decreasing the number of machines). In order to make up for this shortcoming, a multi-objective flexible job shop integrated scheduling problem including new machine insertion, old machine scrap, processing speed selection and predictive maintenance(PdM) is studied, which includes new machine insertion, old machine scrap, processing speed selection and predictive maintenance(PdM). The specific research contents include: ① a multi-phase-multi-threshold PdM policy with processing speed selection, variable PdM thresholds and sixteen discrete inspection policies is designed based on the accelerated degradation Gamma process; ② an adaptive rescheduling strategy(ARS) is constructed for new machine insertion, old machine scrap and the scale of production scheduling problem; and ③ an adaptive bi-population cooperative multi-objective evolutionary algorithm (ABCMOEA) is proposed to address the concerned problem. In the numerical simulation, the effect of parameter settings on ABCMOEA algorithm is firstly investigated. Secondly, the effectiveness of proposed cooperative initialization method and three local search mechanisms is demonstrated by literature comparisons. Thirdly, the superiority of proposed ABCMOEA algorithm, PdM policy and ARS strategy is separately verified by comparing with other intelligent algorithms, PdM policy and rescheduling strategies. Finally, through sensitivity analysis, it is found that the selectable range of processing speed and maintenance level has a significant impact on the research of integrated optimization, and the former has a greater impact.

Key words: flexible job-shop scheduling, new machine insertion, old machine scrap, processing speed selection, maintenance level selection, predictive maintenance, multi-objective evolutionary algorithm

中图分类号:

TP273

安友军, 刘成, 陈晓慧, 高开周, 董元发, 孟荣华. 具有设备动态性和加工速度选择的生产与维护集成调度研究[J]. 机械工程学报, 2025, 61(14): 362-382.

AN Youjun, LIU Cheng, CHEN Xiaohui, GAO Kaizhou, DONG Yuanfa, MENG Ronghua. Research on Integrated Scheduling of Production and Maintenance with Machine Dynamics and Processing Speed Selection[J]. Journal of Mechanical Engineering, 2025, 61(14): 362-382.

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