串联生产系统维护在线决策与缓冲分配联合优化
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作者:
作者单位:

同济大学 机械与能源工程学院,上海 201804

作者简介:

陆志强(1968—),男,教授,博士生导师,工学博士,主要研究方向为物流与供应链建模与优化等。 E-mail: zhiqianglu@tongji.edu.cn

中图分类号:

TH17

基金项目:

国家自然科学基金(61473211)


Joint Optimization of On-Line Decision-Making for Maintenance and Buffer Allocation for Serial Production System
Author:
Affiliation:

School of Mechanical and Energy Engineering, Tongji University, Shanghai 201804, China

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    摘要:

    针对生产系统的退化状态不能在线获取的问题,提出了设备维护在线决策与缓冲分配的联合优化模型。以隐马尔科夫退化系统的工件质量指标为决策依据,提出了设备维护的在线决策策略;推导了串联生产系统工件加工时间与完成时间的递推式,建立了在有限缓冲容量下的缓冲分配模型。以最小化总成本为优化目标,建立了以执行设备维护的质量阈值与缓冲分配为联合决策变量的数学模型。以基于设备跃迁过程的蒙特卡洛仿真算法估计系统期望成本,采用禁忌搜索算法对模型求解,并提出元胞自动机制邻域规则优化搜索过程。数值实验表明提出的联合优化模型及算法的有效性。

    Abstract:

    Aimed at the problem that the degradation state of equipment cannot be obtained online for serial production system, a joint optimization model of maintenance、quality control and buffer allocation was proposed. Based on the quality index of the hidden Markov degradation system, an online decision-making strategy for maintenance was proposed. The recursive formula of processing time and completion time was derived, and the buffer allocation model with limited buffer capacity was established. In order to minimize the total cost, a joint optimization mathematical model to decide the optimum quality threshold for maintenance and buffer allocation was established. The Monte Carlo simulation algorithm based on machine state-transition was used to estimate the expected cost of the system and a tabu search algorithm with cellular automatic mechanism neighborhood rules was proposed to solve the model. The numerical experiments verify the validity of the proposed joint optimization model and algorithms.

    图1 带有质量检查工位与缓冲区的生产线示意图Fig.1 Serial production system with quality inspection stations and intermediate buffers
    图2 设备维护在线决策机制Fig.2 Online decision-making strategy of maintenance
    图3 设备停机及缓冲区库存与其上下游设备关系Fig.3 Relationship between buffer stock and breakdowns of upstream and downstream machines
    图4 累积均值及上下偏差随仿真重复次数变化Fig.4 Cumulative mean, and upper and lower deviation with replications of simulation
    图5 禁忌搜索编码方案示意图Fig.5 Coding example of tabu search algorithm
    图6 禁忌搜索算法流程图Fig.6 Flowchart of Tabu search algorithm
    图8 不同策略下的总成本构成Fig.8 Component of total cost of different strategies
    图9 不同策略下不同参数与总成本的关系图Fig.9 Diagram of parameters and total cost of different strategies
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陆志强,张之磊.串联生产系统维护在线决策与缓冲分配联合优化[J].同济大学学报(自然科学版),2021,49(3):431~439

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  • 收稿日期:2020-09-28
  • 在线发布日期: 2021-04-06
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