带启动时间和工作故障的M/M/1/N排队系统性能分析

在M/M/1/N可修排队系统中引入了工作故障和启动时间.服务台在忙期允许出现故障,且在故障期间不是完全停止服务而是以较低的服务速率为顾客服务.同时,从关闭期到正规忙期有服从指数分布的启动时间.通过分析此模型的二维连续时间Markov过程,求解出系统平稳方程,建立此系统的有限状态拟生灭过程(QBD).根据系统参数,求解出水平相依的子率阵,从而得到系统稳态概率向量的矩阵几何表示形式.在系统稳态概率向量的基础上,求解出系统吞吐率、系统稳态可用度、系统稳态队长及系统处于各个状态的概率等性能指标的解析表达式.文中的敏感性分析体现了这种方法的有效性和可用性,同时,对系统各性能受系统参数的影响进行了探索.实验表明,文中提出模型的稳定性较好,且更贴近实际服务过程,因此这种模型将被广泛应用于各种实际服务中.     

基于几何可靠性机器模型的装配系统实时性能分析

装配系统是生产系统的基本结构之一, 广泛应用于汽车、电器、电子产品等实际生产环境中.与传统的串行生产线取得的研究成果相比, 装配系统的研究, 特别是对系统暂态过程的实时性能分析的研究仍然未得到深入探讨.本文针对具有三台几何可靠性机器模型和有限缓冲区容量框架下的装配系统, 首先建立了用于此类系统暂态性能分析的数学模型, 通过马尔科夫方法导出了系统性能分析的解析公式.然后, 提出了一种基于分解的性能评估算法来近似系统的实时性能.具体来说, 本文推导出了用于计算具有三台几何可靠性机器模型的装配系统的实时生产率、消耗率、在制品数量, 以及完成一个生产批次所需时间的解析表达式.最后, 通过数值实验对所提出算法的准确性进行验证.     

带启动时间和可修服务台的M/M/1/N工作休假排队系统

分析带有启动时间、服务台可故障的M/M/1/N单重工作休假排队系统.在该系统中,服务台在休假期间不是完全停止工作,而是处于低速服务状态.假定服务台允许出现故障且当出现故障时,服务台停止为顾客服务且立即进行修理.服务台的失效时间和修理时间均服从指数分布,且工作休假期和正规忙期具有不同的取值;同时,从关闭期到正规忙期有服从指数分布的启动时间.建立此工作休假排队系统的有限状态拟生灭过程(QBD),使用矩阵几何方法得到QBD的各稳态概率相互依赖的率阵,从而求得稳态概率向量.通过有限状态QBD的最小生成元和稳态概率向量得到系统的基本阵和协方差矩阵,求解出系统方差、系统稳态可用度、系统吞吐率、系统稳态队长、系统稳态故障频度等系统性能.数值分析体现了所提出方法的有效性和实用性,通过敏感性分析将各参数对系统性能的影响进行了初探,为此模型的实际应用提供了很好的理论依据.     

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.     

基于辅助模型正交匹配追踪的多输入系统迭代辨识算法

针对含有未知时滞的多输入输出误差系统的时滞与参数辨识问题,提出一种基于辅助模型的正交匹配追踪迭代算法.首先,由于各输入通道的时滞未知,通过设定输入回归长度,对系统模型进行过参数化,得到一个高维的辨识模型,且辨识模型中参数向量为稀疏向量;然后,基于辅助模型思想和正交匹配追踪算法,在每次迭代过程中,对参数向量和辅助模型的输出进行交互估计,即利用正交匹配追踪算法获得参数向量的估计,再利用参数估计值计算辅助模型的输出,并用辅助模型的输出值代替信息向量中的不可测信息项以更新参数估计;最后,根据参数向量的稀疏特征,获得系统的时滞估计.所提出算法可以利用少量的采样数据信息同时获得系统参数和时滞的估计值.仿真结果表明了所提出算法的有效性.     

基于加权HMM的车辆电源系统状态预测

针对车辆电源系统状态趋势问题,提出了一种加权隐马尔可夫模型的状态预测方法。通过建立电源系统的隐马尔可夫模型,利用加权预测思想对隐马尔可夫模型中隐状态序列进行预测,将最大概率隐状态利用观测概率密度计算出状态观测值。通过对电压调节脉宽信号的导通率进行预测,并与BP神经网络和自回归（AR）模型对相同序列的预测结果进行对比,结果表明该方法对系统的状态变化具有较好的预测能力。     

依托站点状态的两级轮询控制系统时延特性分析

基于区分业务优先级和提高系统时延性能的网络需求,提出了依托站点状态的两级轮询控制系统.系统在混合服务两级轮询模型的基础上,根据站点缓冲区状态采用并行调度方式仅对有数据分组的活动站点提供服务.该模型既能满足区分站点优先级的需求又能避免空闲查询,从而提高系统利用率、降低等待时延.采用嵌入式马尔科夫链和概率母函数的方法对该系统建立数学模型,对系统平均等待时延特性进行了精确解析.通过理论计算与仿真实验结果的对比验证了理论分析的正确性,与已有两级轮询系统相比,具有更好的时延性能.     

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.     

基于马尔可夫过程的多部件系统劣化状态空间划分模型

工业系统的复杂性及智能化程度的不断提高使得系统的可靠性和安全性严重制约着系统正常的工作运转.长时间的工作运行会加大系统的故障风险,降低其安全稳定性.为了减少系统故障给产品质量和生产成本带来的影响,系统的最优维修决策问题逐渐成为研究的热点.分析系统的劣化状态有利于对系统做出正确的维修决策,延长系统的运行时间及减少经济损失.针对相同且劣化独立的多个部件组成的系统,建立离散状态建模下的多部件系统的多状态联合劣化空间划分模型.通过联合劣化状态空间的划分,给出所有维修需求组合及其概率计算通式,运用马尔科夫过程理论建立系统状态的平稳概率模型,并通过数值实验验证了该模型的正确性和有效性.     

带工作休假和工作故障的M/M/1/N排队系统性能分析

本文在可修M/M/1/N排队系统中引入了启动时间、工作休假和工作故障策略.在该系统中,服务台在休假期间不是完全停止工作,而是处于低速服务状态.设定服务台在任何时候均可发生故障,当故障发生时立刻进行维修.且当服务台在正规忙期出现故障时,服务台仍以较低的服务速率为顾客服务.服务台的寿命时间和修理时间均服从指数分布,且在不同的时期有不同的取值.同时,从关闭期到正规忙期有服从指数分布的启动时间.本文建立此模型的有限状态拟生灭过程(QBD),使用矩阵几何方法得到系统的稳态概率向量,并应用基本阵和协方差矩阵理论,计算出系统稳态可用度、系统方差、系统吞吐率、系统稳态队长及各系统稳态概率等系统性能指标.同时,通过数值实验对各系统参数对系统性能的影响进行了初探.文中的敏感性分析体现了这种方法的有效性和可用性.实验表明,文中提出的模型,可有效改善仅带有工作休假或工作故障策略排队模型的系统性能.     

The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, and setup times) is viewed as a “robust” policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This could provide production engineers a guideline in operation management.     

Research on optimization of setup time for product in the multi-product production system

Consumers' consumption habits are more and more personalized and diversified, which makes the multi-product production system has been applied extensively in the factory worldwide. This brings a difficult problem to a large number of manufacturing enterprises: how to optimize the setup time of the product to achieve the purpose of improving the time efficiency. Based on this problem, this paper proposes the TCP technology for the optimization of setup time, that is, using the Times Series model, the Clustering Algorithm, and the Parallel Job technology in the Single Minute Exchange of Die (SMED), to form an application framework focusing on optimizing the product setup time. The validity of the technology is verified by a case study. This paper enriches the research field of setup time optimization, production planning, and the application of the Clustering Algorithm in the multi-product production system. It provides a new way for manufacturing enterprises to pursue an excellent efficiency of product setup time.     

Effect of product mix on multi-product pull control

Product mix influences the performance of pull production control strategy in multi-product manufacturing systems. The complexity of product mix on the performance of a manufacturing system is primarily based on the characteristics of the demand and production control strategies. Demands are mainly characterised by volume and product-type while production control strategy is characterised by material release time, part flow, inventory control and throughput times. In multi-product systems, pull production control strategy operates dedicated or shared Kanban allocation policy. This paper examines the performance of the Generalised Kanban Control Strategy (GKCS), Extended Kanban Control Strategy (EKCS) and Basestock Kanban-CONWIP (BK-CONWIP) control strategy operating Shared Kanban Allocation Policies (S-KAP) or Dedicated Kanban Allocation Policies (D-KAP) for a healthcare parallel/serial assembly line with setup times. A simulation based multi-objective optimisation technique was adopted to examine the effect of different product mixes on the strategies and policies. A ranking and selection technique for multiple systems was used to screen the performance of the strategies. It was shown that product mix variability in a system influence the inventory levels of the pull control strategies examined. However, the performances of the strategies vary with strategies operating S-KAP having better inventory control than strategies operating D-KAP. Similarly, BK-CONWIP outperformed its alternatives.     

A multi-machine multi-product EPQ problem for an imperfect manufacturing system considering utilization and allocation decisions

This paper considers a multi-product problem with non-identical machines. This manufacturing system consists of various machine types with different production capacities, production costs, setup times, production rates and failure rates. One of the major issues in the planning phase of a manufacturing system is to take the best decision about which machines must be utilized to manufacture which items. As a result, the decision makers face three critical questions: what machines must be purchased, which items should be allocated to each machine, and what is the optimal cycle length. These decisions must be made to minimize system costs including utilization, setup, production, holding and scrap costs. The multi-machine multi-product economic production quantity (EPQ) problem for an imperfect manufacturing system is formulated as a mixed integer non-linear programming (MINLP), where the convexity property of multi-product single machine EPQ model is used to convert the problem into a bi-level decision-making problem. In the first level, decisions about machine utilization and items allocation are made. After, in the second level the optimal cycle length for each machine is determined. To solve the problem at hand, a hybrid genetic algorithm (HGA) is proposed integrating genetic algorithm and derivatives method. In the proposed HGA, the solutions of the first level are obtained randomly and then, for the second level, the derivatives method is applied to obtain optimal cycle length based on solutions of the first level. Finally, the results of HGA method are compared to the results of general algebraic modeling system (GAMS) and it is found that HGA method has better and more efficient results. Also, a numerical experimentation and a sensitivity analysis of the model are done.     

Dynamic routing in reentrant flexible manufacturing systems

Consider a flexible manufacturing system (FMS), with several parallel production lines. Each line is statistically balanced. Due to process time and yield variations, some workstations may be temporarily starved of parts during the FMS operation, while others may have too many. The purpose of the dynamic routing algorithm described here is to achieve real-time load balancing in a stochastic processing environment and thus to increase the performance of the system in throughput, workload balance and reduced work-in-process queues. We formulate the problem and develop an optimal stationary policy (for two lines that have a material handling transport between them) based on the input buffer state of each station.     

A multi-attribute decision model for setting production planning parameters

Production and inventory-related decisions, which significantly influence each other and sometimes involve multiple attributes, trade-off assessment and uncertainties, serve a key role in the performance of make-to-stock (mts) manufacturing systems that are controlled by a constant work in process (conwip) order release policy. To benefit from established production planning methods, a crucial task in this context is to define suitable production parameter settings for a given planning horizon. To address this problem, we present a multi-attribute decision model to determine appropriate settings for the planning parameters, namely, cycle time, throughput rate, holding cost and stockout cost. The proposed model uses discrete event simulation to evaluate the performance of a conwip/mts manufacturing system in relation to the work in process and finished goods inventory. Analysis of variance (ANOVA) and a Kruskal-Wallis test are conducted to verify the significant effect on the analyzed parameters. The compromise solution that is recommended for the conwip/mts problem is obtained by considering a multi-attribute expected utility function that is representative of a decision maker’s preferences and risk attitude regarding the probability distribution of the simulation outputs. In contrast with preview studies on planning parameter setting, the result compensates the low performance of one of the attributes as a result of the high performance of another attribute, based on the axiomatic structure of MAUT.Based on the real data of a multi-product assembly line, a numerical application is employed to visualize the steps of this decision model and to demonstrate its usefulness in practical issues.     

Modelling and performance maximization of an integrated automated guided vehicle system using coloured Petri net and response surface methods

The objective of this study is to model and maximize performance of an integrated Automated Guided Vehicle System (AGVS), which is embedded in a pull type multi-product, multi-stage and multi-line flexible manufacturing system (FMS). This study examines the impact of guide-path flexibility on system performance through the development of three different guide-path configurations which range from dedicated to flexible relationships between automated guided vehicles (AGVs) and machine/assembly station resources. The system is modelled using coloured Petri net method (CPN) and the simulation results lead to identify the resource redundancy which can be rectified to achieve lower overall cost of the system through the development of flexible guide-path configurations. The study is extended to seek global near-optimal conditions for each guide-path configuration using response surface method, which yields improvements in system throughput and cycle time along with a decrease in the numbers of AGVs.