| 2 | Exploring self-organization and self-adaption for smart manufacturing complex networks显示文摘Trends toward the globalization of the manufacturing industry and the increasing demands for small-batch,short-cycle,and highly customized products result in complexities and fluctuations in both external and internal manufacturing environments,which poses great challenges to manufacturing enterprises.Fortunately,recent advances in the Industrial Internet of Things(IIoT)and the widespread use of embedded processors and sensors in factories enable collecting real-time manufacturing status data and building cyber–physical systems for smart,flexible,and resilient manufacturing systems.In this context,this paper investigates the mechanisms and methodology of self-organization and self-adaption to tackle exceptions and disturbances in discrete manufacturing processes.Specifically,a general model of smart manufacturing complex networks is constructed using scale-free networks to interconnect heterogeneous manufacturing resources represented by network vertices at multiple levels.Moreover,the capabilities of physical manufacturing resources are encapsulated into virtual manufacturing services using cloud technology,which can be added to or removed from the networks in a plug-and-play manner.Materials,information,and financial assets are passed through interactive links across the networks.Subsequently,analytical target cascading is used to formulate the processes of self-organizing optimal configuration and self-adaptive collaborative control for multilevel key manufacturing resources while particle swarm optimization is used to solve local problems on network vertices.Consequently,an industrial case based on a Chinese engine factory demonstrates the feasibility and efficiency of the proposed model and method in handling typical exceptions.The simulation results show that the proposed mechanism and method outperform the event-triggered rescheduling method,reducing manufacturing cost,manufacturing time,waiting time,and energy consumption,with reasonable computational time.This work potentially enables managers and practitioners to implement active perception,active response,self-organization,and self-adaption solutions in discrete manufacturing enterprises. | Zhengang GUO Yingfeng ZHANG Sichao LIU Xi Vincent WANG Lihui WANG | 2023 | Frontiers of Engineering Management2023，10，2： | 0 |
| 3 | 面向工业互联网平台的二维制造服务协作优化显示文摘工业互联网平台被公认为智能制造的必要推动者,使物理制造资源得以虚拟化,并允许资源以服务的形式进行协作。作为平台的核心功能,制造服务协作优化致力于为制造任务提供高质量的服务协作解决方案。这种优化与任务的功能和数量要求密不可分,在编排服务时必须满足这些要求。然而,现有的制造服务协作优化方法主要关注服务之间针对功能需求的横向协作,很少考虑纵向协作来覆盖所需的数量。为了解决这一差距,本文提出了一种结合功能和数量协作的二维服务协作方法。首先,提出了一种描述服务的多粒度制造服务建模方法。在此基础上,建立了二维制造服务协同优化模型。在垂直维度上,多个功能等效的服务组成一个服务集群来完成一个子任务;在水平维度上,互补服务集群协作完成整个任务。服务的选择和所选服务的金额分配是模型中的关键问题。为了解决这个问题,设计了一种具有多个局部搜索算子的多目标模因算法。将该算法嵌入竞争机制来动态调整本地搜索算子的选择概率。实验结果表明,与常用算法相比,该算法在收敛性、解质量和综合度量方面具有优势。 | Shibao Pang Shunsheng Guo Xi Vincent Wang Lei Wang Lihui Wang | 2023 | Engineering2023，，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员