可用度约束条件下的设备不完美预防维修策略

针对2M1B生产系统,基于设备实际维修情况,提出了故障设备不完美预防维修策略。首先,考虑设备随时间不断劣化的情况,基于准更新过程,建立了生产周期内设备随机故障次数的表达式,计算了设备维修总费用。其次,通过分析缓冲区内库存的变化轨迹,以生产周期内设备随机故障次数为基础,计算了缓冲区库存费用,综合设备维修费用和缓冲区库存费用,构建了周期内生产总成本模型。以满足系统最低要求的可用度水平为约束条件,以预防维修周期和缓冲区库存量为决策变量,以生产周期内单位时间总成本为目标函数,通过离散迭代算法获得最优预防维修周期和此周期下的最佳缓冲区库存量。最后,通过数值分析验证了模型的有效性,为制定最佳维修策略提供了有效依据。     

等待时间有限的串行生产系统的缓冲区优化

对于等待时间有限的串行生产系统,由于上下游设备会发生随机故障,若两个连续设备之间的缓冲区过大,则会产生较大的返工成本,缓冲区过小则会产生较大的产能损失成本.在保证产品质量的前提下,为了有效地降低系统运作成本,提出等待时间有限的串行生产系统的缓冲区优化模型.基于工件排队等待时间有限制这一生产线特点,分别分析预防性维护成本、返工成本和产能损失成本,以单位时间总成本最小为优化目标,建立一个成本函数的分析模型,得到最优缓冲区阈值.最后通过数值计算结果表明,利用成本函数的分析模型能够确定最优缓冲区阈值,从而有效降低运行成本.     

可重入生产系统的QBD型模型

考虑可重入生产系统除第一个外均为有限缓冲区的情形,建立了两种两站四缓冲区的 拟生灭过程(QBD)型模型.系统在随机调度策略下状态集是不可约的,而在最后一个缓冲区先 加工(LBFS)的策略下状态集是可约的.将可约的状态集化成不可约的吸收集和可约状态集的 和.求出了系统状态的稳态分布,给出了系统稳定的充要条件.     

考虑缓冲区故障的多产品生产系统的性能分析

对带有生产准备时间和缓冲区故障且生产时间服从任意分布的多产品生产系统进行分析,通过构建系统状态向量,建立其离散时间马尔可夫链模型.根据多产品生产系统的工作过程对系统状态向量分4种情况进行讨论,计算出系统的状态转移矩阵;在计算系统稳态向量的基础上获得系统生产率的计算流程.根据所建立的模型,对参数对称且可生产两种产品的多产品生产系统进行详细求解,给出其系统生产率的计算过程,通过Matlab进行数学实验,分析总结出缓冲区的无故障率、产品到达率、生产准备时间、服务率及其变异系数对系统生产率的影响.     

排队论在ATM网络传输中的应用

将排队论应用于ATM网络传输系统中,针对ATM网络传输过程中的IP块时延的抖动问题,提出了一种新型的缓冲区策略,并且通过仿真,计算出了该策略下缓冲区的最优值.最后结果表明,这种缓冲区策略能够有效降低由于抖动而产生的数据丢失率,在网络状态欠佳的情况下,也能够保证数据的正常传输.对于ATM网络传输系统的缓冲区方案设计及其参数确定具有较大的理论意义和很强的实用价值.     

基于退化机器模型的分布式柔性生产系统:性能分析、任务调度及预测性维护

在近些年的制造环境中,由于市场对多品种、小批量定制产品需求的增加,生产制造更加深入地向着柔性方向发展.如何利用现有资源,提高生产效率,实时地对系统性能进行评估与预测,并对基于小批量生产的实时调度进行优化改进,在分布式柔性生产系统中具有重要的研究意义.因此,基于退化机器模型的多批次串行生产线的性能进行分析,并对分布式生产系统进行任务调度及预测性维护.具体地说,对于具有退化机器模型及有限容量缓冲区的生产系统,首先采用马尔科夫分析方法建立数学模型;随后,提出精确方法来计算此生产系统模型实时的性能指标,并针对该模型下的调度问题,设计最优完成时间指标优化算法;此外,提出基于退化机器模型的预测性维护策略以减少完成时间;最后,通过数值实验验证该算法的可行性和有效性.     

印染行业不可靠生产系统生产控制模型

基于印染行业自身特性,建立了适合于该行业特点的不可靠生产系统的生产控制模型。该模型不但考虑了生产设备时有故障和修复事件的发生,而且也把由于生产系统操作条件及生产原料属性的波动造成合格产品呈随机分布的情况纳入模型框架,其中当不同产品间生产切换时,所需切换时间及切换费用也被引入模型,并假设此时设备的故障过程为Markov过程。结合单设备单产品情况,给出了其最优生产控制策略。为实现印染行业生产的优化控制及建立完善的MES系统提供了理论支持。     

基于马尔科夫决策过程的ATO系统独立组件与产品双需求最优决策研究

研究多维组件, 单一产品的双需求型面向订单装配(Assemble-to-order, ATO)系统. 产品需求为延期交货型, 当其不被满足时将产生缺货等待成本; 而独立组件需求为销售损失型, 其不被满足时将产生缺货损失成本. 该问题可以抽象成一个动态马尔科夫决策过程(Markov decision process, MDP), 通过对双需求模型求解得到状态依赖型最优策略, 即任一组件的最优生产--库存策略由系统内其他组件的库存水平决定. 研究解决了多需求复杂ATO系统的生产和库存优化控制问题. 提出在一定条件下, 组件的基础库存值可以等价于最终产品需求的库存配给值. 组件的基础库存值与库存配给值随系统内其他组件库存的增加而增加, 而产品需求的库存配给值随系统组件库存和产品缺货量的增加而减少. 最后通过数值实验分析缺货量及组件库存对最优策略结构的影响, 并得到了相应的企业生产实践的管理启示.     

考虑缺货的模糊随机缺陷生产系统

针对不可靠的生产过程,研究了生产故障时间为模糊随机变量且允许缺货的缺陷生产系统.建立含缺货费和模糊随机重修费的经济生产批量模型.基于可信性理论,建立其期望费用模型,揭示了费用函数的性质,并证明了使费用最小的最优生产时间的存在性和唯一性,从而确定了最优生产时间的上下界.基于此,设计了最优生产时间的二分法求解过程.最后通过算例验证了所提出模型的有效性,并分析了缺货费用、重修费用和缺陷产品比例对最优生产策略的影响.     

一种多周期随机需求生产/库存控制方法

为了合理地对库存进行管理,使得产品的存贮、生产和缺货等费用的总和最小,建立了一种多周期随机需求生产／库存模型．该模型采用（s,Q）策略对生产和库存进行控制,即当成品库存降至S时准备生产,生产量为Q．通过对模型费用函数特性的分析,设计了一种最优生产控制算法,根据该算法可以得出系统的最优生产准备点和最优生产量．理论分析和计算结果表明,该方法可以有效地减小系统生产和库存的平均费用．     

基于仿真的生产设备预防性维修周期研究

为了对生产系统中的设备故障加以有效的控制，从而减少设备故障的发生，文章对生产设备的预防性维修周期问题进行了研究。通过建立生产设备预防性维修的费用模型，利用仿真的方法对不同维修策略的预防性维修周期进行优化选择。该模型综合考虑了修复性维修成本、预防性维修成本和生产损失成本。最后以故障分布形式为威布尔分布的设备为例进行仿真实验，得出设备最优维修周期结果，并阐述了经济原则对预防性维修周期的影响。     

考虑中间库存缓冲区的设备不完美预防维修策略研究

针对带有库存缓冲区的设备维修问题,提出了随机故障设备的不完美预防维修策略。首先,考虑设备随机故障率随故障次数的增多而变大的情况,基于准更新过程,建立随机故障设备的故障次数表达式。其次,结合设备故障次数表达式,综合考虑维修成本和库存缓冲成本的基础上,构建了设备生产成本模型,以缓冲库存量和预防维修周期为自变量,以生产成本为目标函数。获得设备的最优维修策略和最佳库存缓冲量。最后,通过算例分析验证了模型的有效性,为生产线设计的可行性提供了依据。     

基于二阶段时间延迟的多产品生产系统生产与维修联合优化研究

针对多产品生产部件串联系统的生产和维修问题进行了研究,提出了基于二阶段时间延迟的联合优化模型。首先,基于生产周期分段理论,将整个周期等分成若干单位时间段,生产与维修共用每段时间,且若干时间段后采取一次预防维修。其次,考虑生产系统的实际生产时间、可用生产时间和维修耗费时间,建立了生产计划与维修计划总成本模型。其中,维修计划考虑缺陷和故障维修费用、维修检查费用,以及非正常状态下设备运行可能产生的不合格产品损失费用;生产计划考虑生产成本、库存成本、延期未交货成本和维修停机后恢复生产的设备启动成本。最后,通过算例分析,计算最优预防维修周期和各单位时间段各产品产量,验证了模型的有效性。     

Buffer allocation and preventive maintenance optimization in unreliable production lines

In this paper, we consider a serial production line consisting of \(n\) unreliable machines with \(n-1\) buffers. The objective is to determine the optimal preventive maintenance policy and the optimal buffer allocation that will minimize the total system cost subject to a given system throughput level. We assume that the mean time between failure of all machines will be increased after performing periodic preventive maintenance. An analytical decomposition-type approximation is used to estimate the production line throughput. The optimal design problem is formulated as a combinatorial optimization one where the decision variables are buffer levels and times between preventive maintenance. To solve this problem, the extended great deluge algorithm is proposed. Illustrative numerical examples are presented to illustrate the model.     

Joint determination of lease period and preventive maintenance policy for leased equipment with residual value

This paper investigates the influence of the length of the lease period on the maintenance policy for leased equipment with residual value. The length of the lease period increases, however, the lessor’s income increases, and the maintenance cost of the equipment rises as well. Therefore, the lease payment and maintenance service of the equipment are crucial items in the lease contract for the lessor’s profit. If the equipment breaks down within the lease period, minimal repairs will be performed on the equipment and the lessor may incur a penalty cost if the repair time exceeds a pre-specified tolerable time. The imperfect preventive maintenance (PM) actions are carried out when the age of the equipment reaches a controlled-limit during the lease period. Under this maintenance scheme, the mathematical model of profit is constructed and then the optimal maintenance policy and the length of the lease period are obtained such that the expected total profit is maximized. Finally, numerical examples are given to illustrate the effects of the optimal length of the lease period and the maintenance policy for profit model.     

An optimal maintenance policy based on generalized stochastic Petri nets and periodic inspection

Periodic maintenance of equipment is essential for its optimum performance, thereby enabling production efficiency. In the past, studies on preventive maintenance of automated manufacturing systems (AMS) determined the optimal preventive maintenance policy under different performance indexes. Generally, most hypotheses indicate that equipment reliability can be restored to 1.0 through preventive and corrective maintenance. However, in practical application, the implementation of preventive maintenance results in partial deterioration of equipment; moreover, the reliability of equipment cannot be restored to as‐good‐as‐new. In addition, the greater the complexity of connections of the equipment, the greater is the difficulty in determining the timing for preventive maintenance. On account of these characteristics, generalized stochastic Petri nets (GSPN) are well‐suited for the implementation of preventive maintenance. Therefore, this paper applies GSPN for deciding the optimal maintenance policy and constructing models for different levels of maintenance and renewal for an AMS with a serial‐parallel layout. As a result of the application of GSPN, the following optimal maintenance policy for an AMS was obtained in this study: Preventive maintenance conducted at intervals of every 240 hours will reduce cost by 46% as opposed to the practice of replacing defective parts when necessary. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Preventive maintenance scheduling for repairable system with deterioration

Maintenance as an important part in manufacturing system can keep equipment in good condition. Many maintenance policies help to decrease the unexpected failures and reduce high operational cost such as conventional preventive maintenance. But these conventional preventive maintenance policies have the same time interval T that may easily neglect system’s reliability, because the system deteriorates with increased usage and age. Hence, this study has developed a reliability-centred sequential preventive maintenance model for monitored repairable deteriorating system. It is supposed that system’s reliability could be monitored continuously and perfectly, whenever it reaches the threshold R, the imperfect repair must be performed to restore the system. In this model, system’s failure rate function and operational cost are both considered by the effect of system’s corresponding condition, which helps to decide the optimal reliability threshold R and preventive maintenance cycle number. Finally, through case study, the simulation results show that the improved sequential preventive maintenance policy is more practical and efficient.     

需求随机波动下多设备批量生产系统的视情维护策略研究

针对需求随机波动情况下多设备批量生产系统的设备维护问题,提出了一种基于滚动生产计划和设备退化状况的视情维护策略。首先,通过滚动时域规划方法预测不同产品的随机需求并在此基础上以总生产成本最小确定滚动生产计划。其次,在每一滚动生产周期开始前检测系统中各设备的退化水平,利用Gamma过程描述退化增量,以最小维护成本率确定当前退化状态下各设备的最佳维护时间,同时为避免生产过程中断利用提前延后维护策略对预防维护进行动态调整。在系统层,利用生产转换时机对需要维护的组件进行组合维护。再次,引入时间约束和服务水平约束,建立批量生产与视情维护的联合优化模型,以总成本最小为目标,确定实际生产计划和维护计划。最后,通过算例以整个生产计划期内的总成本和故障次数为度量验证了所提出的多设备批量生产系统视情维护策略的有效性。     

Multi-objective imperfect maintenance optimization for production system with an intermediate buffer

In several production systems, buffer stocks are built between consecutive machines to ensure the continuity of supply during interruptions of service caused by breakdowns or planned maintenance actions. However, in previous research, maintenance planning is performed individually without considering buffer stocks. In order to balance the trade-offs between them, in this study, an integrated model of buffer stocks and imperfective preventive maintenance for a production system is proposed. This paper considers a repairable machine subject to random failure for a production system by considering buffer stocks. First, the random failure rate of a machine becomes larger with the increase of the number of random failures. Thus, the renewal process is used to describe the number of random failures. Then, by considering the imperfect maintenance action reduced the age of the machine partially, a mathematical model is developed in order to determine the optimal values of the two decision variables which characterize the proposed maintenance strategy and which are: the size of the buffer stock and the maintenance interval. The optimal values are those which minimize the average total cost per time unit including maintenance cost, inventory holding cost and shortage cost, and satisfy the availability constraint. Finally, a heuristic procedure is used to solve the proposed model, and one experiment is used to evaluate the performance of the proposed methods for joint optimization between buffer stocks and maintenance policy. The results show that the proposed methods have a better performance for the joint optimization problem and can be able to obtain a relatively good solution in a short computation time.