基于复杂网络的工业 5.0 弹性和灵活设计资源分配方法

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nanfeng Ma, Xifan Yao , Kesai Wang
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引用次数: 0

摘要

工业 5.0 的发展注重定制化、个性化生产和员工的创新思维,提升了人的贡献价值。设计作为一个创新驱动的领域,对资源分配的灵活性要求更高。因此,为个性化设计任务快速有效地分配云服务资源变得至关重要。随着工业元宇宙的出现,模糊了真实与虚拟设计和制造之间的界限,它正受到越来越多的关注。为了迎接工业 5.0 和工业元宇宙的到来,必须为设计和制造资源提供迅捷的协作云服务。在此背景下,本文介绍了一种将复杂网络与非支配排序遗传算法 III(NSGA-III)相结合的新方法,旨在快速优化分布式设计资源(DR)的动态分配。首先,根据原始数据创建多方图,并将其映射到多个双方图中,通过交集确定网络中的关键节点。随后,这些关键节点被用作 NSGA-III 算法的参考点,以实现高质量的云服务组合,满足具有多个子任务的设计任务的需求,以及相关的多目标优化,包括与设计相关的时间、成本、可靠性、可维护性和声誉。最后,利用获得的帕累托服务组合构建新的复杂网络,并采用基于边间度的 Girvan-Newman 算法来识别群落结构。在最佳服务组合出现异常的情况下,可以从识别出的群落中迅速寻找替代方案,从而增强云服务流程的弹性。实验结果证明了该方法在恢复性和稳健性方面的优势,对优化工业 5.0 背景下的灾难恢复快速云服务分配大有裨益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A complex network-based approach for resilient and flexible design resource allocation in industry 5.0

The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence of the Industrial Metaverse, which blurs the boundaries between real and virtual design and manufacturing, it is gaining increasing attention. To embrace the advent of Industry 5.0 and the Industrial Metaverse, swift collaborative cloud services for design and manufacturing resources are essential. In this context, this article introduces a novel approach combining complex networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), aimed at rapidly optimizing the dynamic allocation of distributed design resources (DRs). Initially, a multipartite graph is created from raw data and mapped to multiple bipartite graphs to identify key nodes in the network through intersection. Subsequently, these key nodes are used as reference points in the NSGA-III algorithm to achieve high-quality cloud service combinations, meeting the needs of design tasks with multiple subtasks, and related multi-objective optimization, including time, cost, reliability, maintainability, and reputation associated with the design. Finally, the Pareto service combinations obtained are used to construct a new complex network and employ the Girvan-Newman algorithm based on edge betweenness to identify community structures. In case of anomalies in the best service combination, alternative options can be swiftly searched from the identified communities, thereby enhancing the resilience of the cloud service process. Experimental results demonstrate the method's advantages in recovery and robustness, contributing significantly to the optimization of rapid cloud service allocation for DRs in the context of Industry 5.0.

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来源期刊
Computers in Industry
Computers in Industry 工程技术-计算机:跨学科应用
CiteScore
18.90
自引率
8.00%
发文量
152
审稿时长
22 days
期刊介绍: The objective of Computers in Industry is to present original, high-quality, application-oriented research papers that: • Illuminate emerging trends and possibilities in the utilization of Information and Communication Technology in industry; • Establish connections or integrations across various technology domains within the expansive realm of computer applications for industry; • Foster connections or integrations across diverse application areas of ICT in industry.
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