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.     

有限小批量定制生产伯努利三机器装配系统实时性能分析

通常适用于大批量制造生产系统的稳态分析在过去几十年得到了广泛研究.然而,当生产量较小（例如,定制化有限小批量生产运行）时,暂态在生产过程中可能起到主要作用,稳态分析将变得不再适用.近年来,对有限小批量生产条件下的串行生产线的研究已经有了一些初步成果.与此同时,装配系统,其最终产品往往需要两个或者多个组件,也广泛用于实践生产中.本文中,在有限小批量生产运行的三机装配系统框架下,假设系统具有有限缓冲区容量,并且使用伯努利机器可靠性模型,研究了此类系统的性能评价问题.具体来说,首先推导出评价系统性能的数学模型和解析公式.然后,提出一种基于分解的方法来近似系统实时性能.最后,所提出的算法的准确性通过仿真数值实验进行了验证并通过一个数值实例进行了展示.     

基于事件驱动的并行及分布式计算机的性能分析系统的设计

介绍了复旦大学研制开发的并行及分布式计算机的分析评估系统的设计方法。为实现系统的通用性并支持面向问题的源相关性，设计了描述事件流的结构、表示方式及语义的事件流述语言，并设计了面向问题的分析评估工具的通用函数接口。从而使建立在此基础上的分析评估工具完全不依赖于监测方式，真正实现了性能分析评估系统的通用性、面向问题及易扩充性等特性。此技术也适用于任何基于事件驱动的监测分析系统。     

基于数值分析的航空发动机装配系统性能评估

航空发动机装配系统是一类比较复杂的装配生产系统,带有工件返件加工和工件可重入等特点,不同于一般的Jobshop和Flowshop的模型,此类复杂的生产系统应用数值分析的方法分析起来比较困难。在文中,以航空发动机装配系统的生产率为研究目标,建立了航空发动机生产系统模型,提出了基于重叠机器分解方法的性能评估算法,并与离散事件仿真结果比较。最后的实验结果表明,该数值分析方法与仿真方法相比具有很高的精确度,大大地缩短了性能评估的时间,能够有效地评估系统性能。     

基于性能退化分析的电连接器可靠性评估

某型电连接器具有寿命长、可靠性高的特点,传统的基于失效寿命数据的可靠性评估方法行不通。提出了基于性能退化分析的可靠性评估方法,给出了进行评估的一般步骤,并以温度为加速应力设计了该型电连接器的加速退化试验。分析了电连接器的接触失效机理,进而推导出退化模型,估计出了样品的伪寿命值。利用Anderson-Darling统计量确定寿命分布类型为对数正态分布,对寿命分布函数和Arrhenius加速方程的参数值进行了极大似然估计,推导出了该型电连接器在工作温度下的可靠度函数,得出t0.9为244 240h。     

基于Zookeeper的分布式锁服务及性能优化

在分布式系统中,资源可能同时被多个客户端申请访问,协调控制客户端和服务器的操作,保证数据访问的正确性和性能是资源的一致性控制要考虑的问题.常用方法是采用定制开发实现其功能或利用现有平台如Hadoop Zookeeper加以实现.Zookeeper是Hadoop的正式子项目,用于提供高效和稳定的一致性服务接口,基于它可以实现分布式锁、配置维护等服务.基于Zookeeper的一致性服务设计实现了分布式锁服务;分析锁服务器端请求处理过程,明确了上锁操作的性能瓶颈在于Zookeeper磁盘IO操作,提出了替换存储介质的方法,Zookeeper上锁性能提高90%;为了进一步减少网络开销,提出分布式锁逻辑移植方法,上锁性能提高20%以上.     

基于分布式混合架构体系的高清智能视频分析系统

针对高清视频流数据量大、对处理平台要求高的特点,提出基于DSP+ARM+PC搭建分布式混合架构体系的硬件平台,研制一种经济适用、性能稳定的高清智能视频分析系统,实现对1080 P及以上的高清视频流进行采集、编码、智能分析、报警、压缩、网络化传输等功能,为高清视频图像的智能分析处理提供一体化的解决方案。     

基于置信规则库的海基系统性能退化机理分析与预测

海基系统性能退化机理分析和预测对于提高海基系统的生存能力具有重要意义,但需要考虑不确定条件下的多种类型信息,传统方法在处理海基系统的不确定性时效果欠佳,而置信规则库(BRB)作为证据推理方法中的知识库又无法同时处理参数精度优化和组合爆炸问题.对此,采用BRB参数与结构联合优化方法,建立双层优化的海基系统置信规则库最优决策结构,以AIC(Akaike information criterion)为外层模型优化目标,MSE(Mean square error)为内层模型优化目标,实现同时优化的目的.对比模型输出和实际输出,并采用支持向量机(SVM)进行实验,结果表明,采用具有最优决策结构的海基系统置信规则库建模不仅可以降低模型中规则的数量,也可提高建模精度,验证了所提出方法的有效性.     

PATCOM:基于分割树的无结构P2P系统一致性维护方法

无结构P2P技术逐渐被应用在新型的协同计算系统中.这些新型业务支持数据的动态更新,不仅要求副本数据的强一致性,而且要求更新数据的快速传播.高效的一致性维护方法是保证新业务顺利开展的基础.在比较分析现有的P2P系统一致性维护方法的基础上,针对无结构P2P系统,提出了一种基于分割树的一致性维护方法--PATCOM.PATCOM使用Chord协议作为组管理协议,通过不断分割由副本节点组成的Chord环,动态地建立更新消息传播树(Update Message Propagation Tree,UMPT).论文进一步从理论上分析了UMPT的平均高度、PATCOM的性能、容错能力以及算法开销,并和基于Gossip的一致性维护方法进行了比较.理论分析和仿真实验结果表明:PATCOM不仅能够快速地维护P2P系统的强一致性,而且产生的冗余更新消息少.     

基于多模式Markov模型的RAID-5系统性能分析

廉价磁盘阵列系统在分布式计算领域得到了广泛的应用,因此仿真廉价磁盘阵列系统的效果是非常必要的.应用马尔科夫模型研究了第五级廉价磁盘阵列的性能.考虑到此系统不能用一个单一的队列模型充分建模,提出了四模式来为其建模.通过Q 仿真软件包对分析模型进行了仿真验证.从马尔科夫模型获得的结果非常接近于Q 仿真结果.另外,还提供几组仿真结果论证了磁盘数量和响应时间之间的关系.仿真结果显示提出的模型是有效的.     

Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.     

Performance analysis for closed-loop production systems with unreliable machines and random processing times

In this study, we propose some simple but effective throughput-approximation methods for finite-buffered closed-loop production systems with unreliable machines and exponentially distributed processing times. The proposed approximation methods are based on decomposition and aggregation principles. According to the method representing the decomposed systems, three different approximation methods are developed, along with a simple upper bounding method for throughputs. Extensive computational experiments are performed to show the effectiveness of the approximation and upper bounding methods.     

Production scheduling of parallel machines with model predictive control

This paper considers the problem of optimizing on-line the production scheduling of a multiple-line production plant composed of parallel equivalent machines which can operate at different speeds corresponding to different energy demands. The transportation lines may differ in length and the energy required to move the part to be processed along them is suitably considered in the computation of the overall energy consumption. The optimal control actions are recursively computed with Model Predictive Control aiming to limit the total energy consumption and maximize the overall production. Simulation results are reported to witness the potentialities of the approach in different scenarios.     

基于柔性维护的堕化单机系统调度研究

针对单机系统,在假设生产系统为堕化系统,且生产过程中作业的加工不可中断的情况下,对考虑柔性时间窗口[[u,v]]下进行长度为[w]的周期预防性维护的调度问题进行了研究。建立了综合考虑生产调度和设备维护的混合整数规划模型,并设计了一套基于贪婪的启发式算法对所研究问题进行优化求解。通过Cplex和启发式算法求解结果的对比证明了算法可以快速、有效地解决此类问题。     

Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning

Predictive Maintenance (PdM) is one of the core innovations in recent years that sparks interest in both research and industry. While researchers develop more and more complex machine learning (ML) models to predict the remaining useful life (RUL), most models are not designed with regard to actual industrial practice and are not validated with industrial data. To overcome this gap between research and industry and to create added value, we propose a holistic framework that aims at directly integrating PdM models with production scheduling. To enable PdM-integrated production scheduling (PdM-IPS), an operation-specific health prognostics model is required. Therefore, we propose a generative deep learning model based on the conditional variational autoencoder (CVAE) that can derive an operation-specific health indicator (HI) from large-scale industrial condition monitoring (CM) data. We choose this unsupervised learning approach to cope with one of the biggest challenges of applying PdM in industry: the lack of labelled failure data. The health prognostics model provides a quantitative measure of degradation given a specific production sequence and thus enables PdM-IPS. The framework is validated both on NASA’s C-MAPSS data set as well as real industrial data from machining centers for automotive component manufacturing. The results indicate that the approach can both capture and quantify changes in machine condition such that PdM-IPS can be subsequently realized.     

两部件系统视情维修与生产调度的联合优化模型

为了解决生产调度过程中由系统维护维修产生的资源闲置和时间成本增加问题,将系统维修与生产调度联合建模.在众多学者将系统作为整体进行生产调度与维修研究的基础上,考虑系统内各组成部件之间的复杂关系.针对具有经济相关性的两部件系统,以调度作业加工顺序、预防性维修阈值、机会维修阈值作为决策变量,考虑到两部件同时维修比单部件独立维...     

Benchmarking flexible job-shop scheduling and control systems

Benchmarking is comparing the output of different systems for a given set of input data in order to improve the system’s performance. Faced with the lack of realistic and operational benchmarks that can be used for testing optimization methods and control systems in flexible systems, this paper proposes a benchmark system based on a real production cell. A three-step method is presented: data preparation, experimentation, and reporting. This benchmark allows the evaluation of static optimization performances using traditional operation research tools and the evaluation of control system's robustness faced with unexpected events.     

A hybrid method for flowshops scheduling with condition-based maintenance constraint and machines breakdown

One of the most important assumptions in production scheduling is that the machines are permanently available without any breakdown. In the real world of scheduling, machines can be made unavailable due to various reasons such as preventive maintenance and unpredicted breakdown. In this paper, we explore flowshop configuration under the assumption of condition-based maintenance to minimize expected makespan. Furthermore, we consider a condition-based maintenance (CBM) strategy which could be used in most industrial settings. The proposed algorithm is designed for non-resumable flowshop state where the processing of jobs after preventive maintenance is restarted from the beginning. We propose a hybrid algorithm based on genetic algorithm and simulated annealing. Additionally, we conduct an extensive parameter calibration with the utilization of Taguchi method and select the optimal levels of the algorithm’s performance influential factors. The preliminary results indicate that the proposed method provides significantly better results compared with other high performing algorithms in the literature.     

Single-machine scheduling with periodic and flexible periodic maintenance to minimize maximum tardiness

This paper considers a single-machine scheduling problem with several maintenances periods. Specifically, two situations are investigated. In the first one, maintenance periods are periodically fixed: maintenance is required after a periodic time interval. In the second one, the maintenance is not fixed but the maximum continuous working time of the machine which is allowed is determined. The objective is to minimize the maximum tardiness. These problems are known to be strongly NP-hard. We propose some dominance properties and an efficient heuristic. Branch-and-bound algorithms, in which the heuristics, the lower bounds and the dominance properties are incorporated, are proposed and tested computationally.     

An effective heuristic for flexible job-shop scheduling problem with maintenance activities

Most production scheduling problems, including the standard flexible job-shop scheduling problem (FJSP), assume that machines are continuously available. However, in most realistic situations, machines may become unavailable during certain periods due to preventive maintenance (PM). In this paper, a flexible job-shop scheduling problem with machine availability constraints is considered. Each machine is subject to preventive maintenance during the planning period and the starting times of maintenance activities are either flexible in a time window or fixed beforehand. Moreover, two cases of maintenance resource constraint are considered: sufficient maintenance resource available or only one maintenance resource available. To deal with this variant FJSP problem with maintenance activities, a filtered beam search (FBS) based heuristic algorithm is proposed. With a modified branching scheme, the machine availability constraint and maintenance resource constraint can be easily incorporated into the proposed algorithm. Simulation experiments are conducted on some representative problems. The results demonstrate that the proposed filtered beam search based heuristic algorithm is a viable and effective approach for the FJSP with maintenance activities.