面向云制造平台的模型修复算法及任务调度算法的研究

随着三维打印技术的发展,面向3D打印服务的云制造平台也得到快速发展,改变着传统的制造模式。然而目前的云制造平台也面临着诸多问题,针对目前存在的模型修复难度高及任务不能合理调度的问题,本文提出了保特征的模型修复算法和基于遗传算法的任务调度算法,并通过实验和仿真验证了算法的有效性。为了更好的体现算法效果,本文搭建了一个基于分布式制造的云制造平台,该平台配合模型自动修复算法以及基于遗传算法的任务调度算法,为用户提供低门槛、高效、优质的3D打印服务。     

基于混沌遗传算法的集团分布式制造工序资源配置

集团分布式制造企业往往存在着地理位置不集中、制造资源和制造能力不均衡、资源闲置与资源短缺并存等问题,针对集团制造企业在制造资源配置过程中多主体、多任务、多资源、多工序以及协同性的特点,从集团公司总体利益及下属企业个体利益多角度出发,综合考虑生产成本、加工资源、加工效率等多个因素,建立集团分布式制造资源配置优化模型,并采用基于Logistic混沌改进的遗传算法求解该模型的Pareto最优解.最后对国内某建材装备集团的制造资源配置过程进行算例分析,以验证模型和算法的有效性.     

一种基于P学习的分布式并行多任务分配算法

并行多任务分配是多agent系统中极具挑战性的课题, 主要面向资源分配、灾害应急管理等应用需求, 研究如何把一组待求解任务分配给相应的agent联盟去执行. 本文提出了一种基于自组织、自学习agent的分布式并行多任务分配算法, 该算法引入P学习设计了单agent寻找任务的学习模型, 并给出了agent之间通信和协商策略. 对比实验说明该算法不仅能快速寻找到每个任务的求解联盟, 而且能明确给出联盟中各agent成员的实际资源承担量, 从而可以为实际的控制和决策任务提供有价值的参考依据.     

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.     

Workload-based multi-task scheduling in cloud manufacturing

Cloud manufacturing is an emerging service-oriented business model that integrates distributed manufacturing resources, transforms them into manufacturing services, and manages the services centrally. Cloud manufacturing allows multiple users to request services at the same time by submitting their requirement tasks to a cloud manufacturing platform. The centralized management and operation of manufacturing services enable cloud manufacturing to deal with multiple manufacturing tasks in parallel. An important issue with cloud manufacturing is therefore how to optimally schedule multiple manufacturing tasks to achieve better performance of a cloud manufacturing system. Task workload provides an important basis for task scheduling in cloud manufacturing. Based on this idea, we present a cloud manufacturing multi-task scheduling model that incorporates task workload modelling and a number of other essential ingredients regarding services such as service efficiency coefficient and service quantity. Then we investigate the effects of different workload-based task scheduling methods on system performance such as total completion time and service utilization. Scenarios with or without time constraints are separately investigated in detail. Results from simulation experiments indicate that scheduling larger workload tasks with a higher priority can shorten the makespan and increase service utilization without decreasing task fulfilment quality when there is no time constraint. When time constraint is involved, the above strategy enables more tasks to be successfully fulfilled within the time constraint, and task fulfilment quality also does not deteriorate.     

基于动态双向优先级的任务分配与调度算法

提出了一种基于动态双向优先级的任务分配与调度算法,称作动态双向优先级(DDDP)算法。该算法综合考虑了实时任务的优先级和子机的优先级,构造了动态双向优先级任务分配模型,实现了数据传输中主机/子机模式的任务动态分配与调度。在模拟实验中,通过使用正常负载和过载情况下的典型数据对算法进行仿真研究表明,这种算法比单纯考虑截止期的EDF算法在性能方面有明显的改进和提高。     

在无中心式作业调度中的动态网格负载平衡实现

提出一个新颖的递归算法,用于实现动态的网格负载平衡。实验仿真了松耦合无中心式调度框架,基于传统并行系统的workload模型构建了网格workload模型,保守式装填法用作各结点上的本地调度策略。结果表明,在实现网格负载平衡上,这里的递归算法比静态调度方法更有效。     

基于Jupyter交互式分析平台的微服务架构

随着Jupyter Notebook在数据科学领域应用规模的不断扩大,对于多用户管理和集群计算资源调度的功能需求越趋增加.本文从Jupyter相关基本概念入手,阐述了Jupyter对于科研成果交流传播的作用影响,总结了目前国外科研机构、高等院校等组织在研究Jupyter分布式架构方面的现状;详细分析了Jupyter体系架构特点,运用微服务的方式重构Jupyter,并通过Kubernetes的资源调度分配算法,实现了基于容器技术的高弹性分布式微服务架构.测试结果数据表明,本文提出的架构在访问负载性能上得到了一定程度的提升,在用户运行数量方面达到了集群上负载均衡的目标.     

基于堆栈处理器的实时多任务调度机制研究与实现

针对堆栈处理器特殊架构,为提高实时性,引入多任务堆栈技术,采用Forth自生成器技术,提出一种基于堆栈处理器的抢占式与时间片轮转调度方法,实现了在Forth堆栈处理器中实时多任务的运行,弥补了Forth堆栈处理器在实时多任务操作系统方面的的不足.实验表明,与当前基于寄存器处理器的嵌入式Forth实时系统相比,本文方法在最大关中断时间、任务上下文切换时间和任务响应时间三项重要的实时任务性能指标方面,实时性能有明显提升,从而保证了Forth系统应用的高效性和安全性,满足人们对Forth堆栈处理器实时多任务操作系统方面的应用需求.     

基于改进粒子群算法的多机器多任务3D打印智能调度方法

针对批量3D打印成本高,多机器多任务的3D打印批次调度复杂的问题,建立以最小单位体积平均成本为目标的优化模型,并提出一种基于改进粒子群算法的智能调度方法求解该模型;首先,分析打印工场、生产流程,构建3D打印单位体积平均成本模型;之后基于改进粒子群算法,以单位体积平均成本为适应度,以调度序列为粒子的位置信息,采用十进制顺序二维编码方式表示问题的解,并在更新策略上应用线性递减权值的动态惯性因子来调整全局与局部的搜索能力;算法迭代后,得到目标函数最优值及对应解集;经实验算例结果表明,该方法较单独打印加工的单位体积平均成本降低了0.101 3GBP/cm3,有效地降低工厂生产的总成本,提高了3D打印机的利用效率。     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

一个分布式实时指挥监视系统

文中介绍一个基于Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒｅ模式的地分布式的一个实用的分布实时数据处理系统，该系统采用了Ｃｌｉｅｎｔ／Ｓｅｒｖｅｒ模式分布系统构造方法和ＷｉｎｄｏｗｓＮＴ提供的多线程抢先式多任务管理方式，引入带优先级的调用策及面向对象的编程技术，从而获得了较好的模块性和实时性。     

A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing

The Industrial Internet of Things (IIoT) interconnects a large number of interconnected sensors, actuators, and edge computing devices in the manufacturing systems, where the massive data collected in the manufacturing process has the characteristics of multi-dimensional, heterogeneous, and time series. An effective data representation manner, which can fuse such complex information and enable cognitive manufacturing decision-making from a global perspective, is necessary and challenging. To solve this issue, this paper proposes a knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing and applies it in a Cyber-Physical Production System (CPPS) scenario. Based on the digital thread of manufacturing process data, a multi-layer manufacturing knowledge graph is established, including device sensing data, production processing data, and business processing data. With the established knowledge graph, a cognition-driven approach is proposed with a perception-cognition dual system, which achieves perception analysis and cognition decision-making in the resource allocation of the manufacturing process. Finally, responding to the orders of personalized products in a workshop is taken as an illustrative example. The performance of allocating resources of workshop devices under dynamic demand changes shows the advantages of the proposed approach. The proposed manner will lay the foundation for a human-like cognition for processing massive real-time industrial information in CPPS, thus paving a pathway towards the era of cognitive manufacturing.     

优先级周期性互换的实时调度算法

针对实时多任务调度时低优先级任务的延迟问题,提出了一种优先级周期性互换的静态优先级调度算法。该方法以固定的时间片为周期,对多任务系统中的某两个不同优先级的独立性任务,周期性地互换它们的优先级级别,在保证较高优先级任务的执行时间的前提下,使得较低优先级的任务有机会尽快执行,以缩短其执行过程中的延迟时间。所提方法能有效解决低优先级任务的实时性问题,从而提高实时多任务系统的整体控制性能。     

基于改进蚁群算法的遥感信息处理负载均衡任务调度算法研究

遥感信息服务链动态构建技术是根据用户提出的航天信息需求,以及用户0终端行为感知后形成的主动推送需求,将遥感信息获取与处理作为一种服务对待,利用服务组合与优化,动态构建服务链,实现网络环境下的信息资源按需聚合与高效协同,以满足对"端"的遥感信息支援应用需求;文章首先研究了蚁群算法和模拟退火算法在遥感信息处理计算节点任务上的调度原理,并分析了上述传统算法在得出最优解之前会出现的问题;基于蚁群算法并结合其他启发式算法的优点,提出了一种基于改进蚁群算法的负载均衡任务调度算法,完成了遥感信息多任务处理服务链的计算任务分配,提升了天基信息处理系统整体的计算效率;最后通过仿真实验验证了算法的有效性.     

Mass personalization strategy under Industrial Internet of Things: A case study on furniture production

The Industrial Internet of Things (IIoT) is characterized by digitalization, networking, and smartness, which opens a world of the interconnection of all things and makes mass personalization possible. As a result, traditional industrial firms are forced to change their operation mode from the manufacture-oriented one to the manufacture-and-service-oriented type. In particular, furniture production is a typical domain featured by mass personalization from networking, where order selection (service aspect) and board cutting (manufacture aspect) are mainly concerned. We formulate customized furniture production as a multi-objective optimization problem and propose two algorithms to solve it, i.e., an integrated algorithm and a two-stage decoupling algorithm. As a secondary, the robust mixed-integer linear optimization algorithm is proposed to deal with the uncertainty such as fluctuations in production capacity and raw materials cost. The numerical experiments using real industrial data demonstrate that the proposed algorithms effectively improve firms’ operational efficiency by achieving a balance between service and manufacture. Moreover, they present remarkable performance under various circumstances. The developed methods could apply to a wide range of mass personalization for related manufacturing scenarios of IIoT with digital servitization, including computer, communication, and consumer electronics products (3C products) machining, automobile accessory production, and chip manufacturing.     

An agent-oriented approach to resolve scheduling optimization in intelligent manufacturing

Agent technology is considered as a promising approach for developing optimizing process plans in intelligent manufacturing. As a bridge between computer aided design (CAD) and computer aided manufacturing (CAM), the computer aided scheduling optimization (CASO) plays an important role in the computer integrated manufacturing (CIM) environment. In order to develop a multi-agent-based scheduling system for intelligent manufacturing, it is necessary to build various functional agents for all the resources and an agent manager to improve the scheduling agility. Identifying the shortcomings of traditional scheduling algorithm in intelligent manufacturing, the architecture of intelligent manufacturing system based on multi-agent is put forward, among which agent represents the basic processing entity. Multi-agent-based scheduling is a new intelligent scheduling method based on the theories of multi-agent system (MAS) and distributed artificial intelligence (DAI). It views intelligent manufacturing as composed of a set of intelligent agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. In this paper, the proposed architecture consists of various autonomous agents that are capable of communicating with each other and making decisions based on their knowledge. The architecture of intelligent manufacturing, the scheduling optimization algorithm, the negotiation processes and protocols among the agents are described in detail. A prototype system is built and validated in an illustrative example, which demonstrates the feasibility of the proposed approach. The experiments prove that the implementation of multi-agent technology in intelligent manufacturing system makes the operations much more flexible, economical and energy efficient.     

Multimedia resource allocation strategy of wireless sensor networks using distributed heuristic algorithm in cloud computing environment

The virtualized resource allocation (mapping) algorithm is the core issue of network virtualization technology. Universal and excellent resource allocation algorithms not only provide efficient and reliable network resources sharing for systems and users, but also simplify the complexity of resource scheduling and management, improve the utilization of basic resources, balance network load and optimize network performance. Based on the application of wireless sensor network, this paper proposes a wireless sensor network architecture based on cloud computing. The WSN hardware resources are mapped into resources in cloud computing through virtualization technology, and the resource allocation strategy of the network architecture is proposed. The experiment evaluates the performance of the resource allocation strategy. The proposed heuristic algorithm is a distributed algorithm. The complexity of centralized algorithms is high, distributed algorithms can handle problems in parallel, and reduce the time required to get a good solution with limited traffic.

     

一种基于Q学习的分布式多任务流调度算法

近来实时动态任务分配机制得到越来越多的研究.考虑多任务流并存时的任务分配问题,提出基于Q学习的分布式多任务流调度算法,不仅能适应自身任务流的到达过程,还充分兼顾其他任务流的到达及分配的影响,从而使得整个系统长期期望回报最大.分布式特性使得算法适用于开放的,局部可见的多Agent系统;强化学习的采用使得任务分配决策自适应系统环境隐藏的不确定性.实验表明此算法具有较高的任务吞吐量和任务完成效率.     

移动群智感知中面向用户区域的分布式多任务分配方法

多数群智感知（MCS）任务分配方法针对单个任务,难以适用于多任务实时并发的现实场景,而且往往需要实时获取用户位置,不利于保护参与者隐私。针对上述问题,提出了一种面向用户区域的分布式多任务分配方法Crowd-Cluster。该方法首先通过贪心启发算法将全局感知任务及用户区域进行分簇;其次,基于空间关联性采用Q-learning算法将并发任务组合构成任务路径;接着,构建符合玻尔兹曼分布的用户意愿模型对任务路径进行动态定价;最后,基于历史信誉记录贪心优选参与者实现任务分配。基于真实数据集mobility的实验结果表明,Crowd-Cluster能有效减少参与者总人数及用户总移动距离,并且在低人群密度场景下,还能降低感知资源不足对任务完成度的影响。