云制造环境下面向多任务协同的机床装备资源动态调度方法

针对云服务环境下支持多任务协同运行的机床装备资源易受紧急插单、系统故障、质量异常等不确定高频扰动影响,致使云制造服务难以高效、稳定运行的问题,提出一种面向多任务协同的机床装备资源动态调度方法。通过应用非线性工艺规划理论,建立了一个包括最小时间、成本、能耗、负载均衡度及最优服务质量和可靠性为约束目标的调度模型,并采用改进遗传变邻域算法对其求解。同时,基于生产随机扰动致使调度方案动态偏离的问题,提出一种基于事件驱动和周期性重调度的动态调度策略,以支持云制造服务的协同优化运行;最后通过仿真案例验证该模型及算法的可行性和适用性。     

机床装备云制造服务建模方法研究

针对云制造环境下机床装备制造服务的共享和集成,在分析机床装备云制造服务建模的特点及需求的基础上,提出一种基于机床装备资源层、机床装备服务语义描述层和机床装备服务实现层三层结构的机床装备云制造服务建模实现框架,基于该框架对机床装备制造资源分析、机床装备制造资源语义描述和机床装备制造服务实现进行研究。通过对一类机床进行云制造服务建模,验证了该建模方法的可行性及有效性。     

机床装备及其加工运行过程云制造服务平台

针对机床装备制造企业从生产型制造向服务型制造转型,以及应用企业基于网络的设备资源共享和远程维修维护服务等迫切需求,提出一种机床装备及其加工运行过程的云制造服务平台,并对该平台的参与角色、体系架构、运行模式及基于网络化信息交互终端的机床装备云端接入、基于本体的机床装备服务能力建模等关键技术进行了研究。最后介绍了该平台在试验环境中的构建和试运行情况。     

云制造环境下基于双层规划的资源优化配置方法

为解决云制造环境下的制造资源优化配置问题,在深入分析云制造的运行模式和制造资源配置过程的基础上,充分考虑云制造环境下制造服务过程中的不确定因素以及制造任务需求方和云制造服务平台运营方的利益,提出包括服务质量指标和柔性指标的云制造资源服务组合评价指标体系,建立了云制造环境下的制造资源优化配置双层规划模型,并采用改进的进化多目标优化算法对模型进行求解,实验结果表明了该模型和算法的可行性和有效性。     

云制造环境下机床装备资源特性分析与语义描述方法

针对云制造环境下机床装备资源具有异质异构、动态演化、高度自治等特性及网络化集成、服务化封装和供需智能匹配等需求,提出并构建了一种基于语义服务建模本体的机床装备资源描述框架,并对支持该框架实现的机床装备资源元数据本体表示方法、机床装备资源服务化封装等关键技术进行了研究。通过应用案例对以上研究成果进行了实验验证。     

基于Multi-Agent的机床装备资源优化选择方法

针对云制造环境下机床装备资源数量大、服务制约因素多、组合优化选择困难等问题,首先建立机床装备资源匹配指标体系和包括服务时间(T)、服务成本(C)、服务质量(Q)、服务知识(K)、服务环境(E)、服务可靠性(R)、服务容错性(Ft)和综合满意度(Sa)八维目标分量的机床装备资源评价指标体系;结合Multi-Agent技术自主响应、智能交互的优势,提出一种基于Multi-Agent的机床装备资源优化选择模型,设计了该优选模型的求解方法并通过实验仿真验证了该方法的适用性和有效性。     

云制造环境下资源动态调度系统研究

云制造环境下资源动态调度是云制造中的关键技术之一。针对云制造环境下制造资源调度的特点和存在的问题,提出了基于资源负载均衡的动态调度策略,建立了以完成任务的总服务成本最小为优化目标的模型,并通过建立应用实例说明了该系统的实际应用过程。     

[云制造]云制造环境下基于贝叶斯网络的机床装备资源优化决策方法

结合复杂网络理论,提出了一种云制造环境下基于贝叶斯网络的机床装备资源优化选择方法。对云制造环境下机床装备资源优化选择问题进行数学描述,构建了一种由决策环境变量、决策选择变量、决策传递变量、决策目标变量、决策价值变量以及因果依赖关系组成的不确定多阶段多目标（UMM）决策模型,并对该模型的求解算法进行研究;通过实例验证了所提方法的适用性和有效性。     

基于多分辨率聚类的云制造任务分配

针对云制造环境下用户需求复杂多变和制造资源能力分散异构的特点,为实现制造资源的动态共享与智能分配,提出一种能够在云制造环境下迅速得出需求能力高效匹配的多分辨率聚类任务分配方案。根据任务目标特点选择适应的参数建立反映需求能力匹配程度的聚类距离函数,并以此距离为聚类依据展开多分辨率聚类,随聚类过程将任务分解并分配到相应的资源能力提供者,得出云制造系统中需求能力的多分辨率层次结构,为解决云制造环境下资源的高效配置问题、实现云制造系统中用户需求和资源能力的动态匹配提供了可行结构。通过实例对以上任务分配方案过程进行了详述,并论证了该算法可为充分利用云制造系统中零碎制造能力提供有效途径。     

云制造环境下的资源调度研究综述

云制造环境下的资源调度是云制造服务中的重要组成部分,它对云制造服务的服务质量有直接影响。云制造环境下的资源调度是根据云资源池中资源的状态并结合智能算法给出最优云服务组合的过程。目前国内外关于云制造环境下的资源调度研究成果主要集中在云制造环境下的车间层资源调度,云制造环境下的资源搜索方法以及云制造环境下基于调度模型的智能算法应用。对目前云制造环境下的资源调度所取的成果进行了综述,同时对云制造环境下的资源调度发展趋势作了展望,为今后对云制造资源调度深入的研究奠定了一定的基础。     

Research on the impact of service provider cooperative relationship on cloud manufacturing platform

As an emerging service-oriented manufacturing paradigm, Cloud Manufacturing (CMfg) is expected to provide smart networked platforms that enable efficient global collaborations among cloud service providers in a product lifecycle. The service provider cooperative relationship (SPCR) plays a crucial part in cloud service scheduling and thus potentially influences the operational performance of a CMfg platform. The guaranteed SPCR could ensure service quality and reduce the interaction costs among service providers in the process of collaborative manufacturing tasks, thereby having a positive impact on the operational efficiency of a CMfg platform. The cost reduction and quality assurance, however, usually make the cost-effective services more attractive for an increasing number of manufacturing tasks and lead to load imbalance in a CMfg platform, thus having a negative impact on the operational efficiency. The impact mechanism of SPCR on a CMfg platform is still unclear from an evolutionary perspective. In this paper, we propose a CMfg platform evolution model (CPEM) to investigate the impacts of SPCR on the operational performance of a CMfg platform. Three quantitative metrics, from the perspective of cloud consumer, cloud operator, and cloud service provider, respectively, are established to support the performance evaluation. The results of contrast experiments reveal the impacts of SPCR on a CMfg platform.     

Multi-task scheduling of distributed 3D printing services in cloud manufacturing

The problem of service matching and scheduling in cloud manufacturing (CMfg) is complex for different types of manufacturing services. 3D printing, as a rapidly developing manufacturing technology, has become an important service form in the CMfg platform due to its characteristics of personalized manufacturing. How to solve the task scheduling problem for distributed 3D printing services in CMfg needs further research. In this paper, a service transaction model of 3D printing services in CMfg is built. Based on the service transaction model, we propose 3D printing service matching strategies and matching rules of different service attributes, including model size, printing material, printing preciseness, task cost, task time, and logistics. To reduce the delivery time of tasks from service suppliers to service demanders, a 3D printing service scheduling (3DPSS) method is proposed to generate optimal service scheduling solutions. In 3DPSS, optimization objective, constraints, and optimization algorithm are presented in detail. Experimental results show that the average task delivery time of 3DPSS is shorter than that of typical scheduling methods, such as particle swarm optimization, pattern search, and sequential quadratic programming, when the amounts of tasks change.     

Research on technologies and application of data mining for cloud manufacturing resource services

Nowadays, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for better implementing CMfg services is how to address the problems faced by distributed massive and polymorphic data analysis and processing of manufacturing resource services in CMfg environment. In order to handle the problems, and promote development integration and business application of data mining, the research on technologies and application of data mining for cloud manufacturing resource (CMR) services has been carried out in this paper. The data mining application model and multi hierarchy architecture of CMR services are designed, and the topology of resource service data integrating process based on multi-Agent is presented. Besides, the preprocessing method of manufacturing resource data is proposed, and a CMR virtual data warehouse is established as well. In addition, an improved genetic algorithm oriented to manufacturing resource services is put forward, so as to achieve efficient searching and mining of massive polymorphic data. Finally, a case study is employed to illustrate the effectiveness and applicability of the proposed method in CMR service platform.     

Application modes of cloud manufacturing and program analysis

Service-oriented manufacturing is a trend in the manufacturing industry in the 21st century. Numerous theories related to this trend have been proposed. In particular, three typical theories, namely, industry 4.0, industrial Internet, and cloud manufacturing (CMfg), are discussed in this paper, including their differences. Existing problems in CMfg are analyzed. From the perspective of the service supplier, and by considering former research, three application modes of CMfg are proposed. A 3D printing application mode of CMfg is presented based on the characteristics of 3D printing and discussed in detail. The key technologies of 3D printing in CMfg are explained according to the differences between traditional manufacturing and CMfg.     

云制造下设计任务与知识资源双边匹配方法

设计任务与知识资源的匹配大多是以业务流程为基础的单向匹配,为解决研发设计任务与知识资源之间的双边匹配问题,提出面向云制造模式的研发设计任务与知识资源双边匹配总体框架,该框架包括双边匹配满意度评价指标体系构建、多指标满意度评价信息处理和多目标双边匹配决策模型构建与求解等关键步骤。以卫星产品研制工艺知识为例,验证了所提方法在解决设计任务和知识资源双边匹配,以及提升产品设计任务执行效率与质量方面的有效性。     

Common engines of cloud manufacturing service platform for SMEs

In order to improve the comprehensive competitiveness of small- and medium-sized enterprises (SMEs), a cloud manufacturing service platform for SMEs was implemented with the idea and model of cloud manufacturing (CMfg) in this paper. Using the transaction mode of multiple enterprises, multiple orders, and multi-party collaboration, the architecture and workflows of this cloud manufacturing service platform were designed. Some common engines, such as intelligent matching engine for supply and demand, transaction coordinating logic engine, and credit evaluation engine, were researched, which implement the semantic intelligent searching, order tracking, transaction task guiding, and collaborative management in the whole process of CMfg. As a case, this platform has been applied in the automobile and motorcycle parts manufacturing industry, which effectively integrates manufacturing service resources in this field through online services and offline services (O2O), achieving the effective management of service resources.     

非稳态云制造服务过程资源异常管理方法

为降低云制造服务过程中由于云制造资源动态变化导致的服务不确定性,构建了非稳态云制造服务过程管理框架,定义了云制造服务与云制造资源之间的关联约束关系,对云制造资源异常事件进行分类定义并建立了单源、多源制造资源异常事件的传播算法与响应机制,开发了非稳态云制造服务过程管控原型系统。在某钢琴制造企业样机试制云服务过程中进行了原型验证应用,结果表明,所提方法能够对影响服务时效的资源异常事件及时作出响应,具有较好的可行性。     

A study of optimal allocation of computing resources in cloud manufacturing systems

As a new advanced service-oriented networked manufacturing model, cloud manufacturing (CMfg) has been proposed recently. The optimal allocation of computing resources (OACR) is a core part for implementing CMfg. High heterogeneity, high dynamism, and virtualization make the OACR problem more complex than the traditional scheduling problems in grid system or cloud computing system. In this paper, a new comprehensive model for OACR is proposed in the CMfg system. In this model, all main computation, communication, and reliability constraints in the special circumstances are considered. To solve the OACR problem, a new improved niche immune algorithm was presented. Associated with the niche strategy, new heuristics are designed flexibly based on the characteristics of the problem and pheromone is added for adaptive searching. Experiments demonstrate the effectiveness of the designed heuristic information and show NIA’s high performances for addressing the OACR problem compared with other intelligent algorithms.