Cloud-edge collaboration composition and scheduling for flexible manufacturing service with a multi-population co-evolutionary algorithm

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2024-06-21 DOI:10.1016/j.rcim.2024.102814

Weimin Jing , Yonghui Zhang , Youling Chen , Huan Zhang , Wen Huang

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引用次数: 0

Abstract

The Cloud Manufacturing Service Composition and Scheduling (CMfg-SCS) are essential processes in cloud manufacturing. Flexible Manufacturing Services (FMS), such as those provided by industrial robots, are widely used in cloud manufacturing to improve service quality and efficiency. Traditional CMfg-SCS methodologies, however, fall short in effectively managing the inherent temporal-dynamic QoS and flexible capability of FMS. To overcome these challenges, we propose a novel Cloud Manufacturing Service Cloud-edge Collaboration Composition and Scheduling (CMfg-SCCCS) method for FMS. Firstly, the service-task matching hypernetwork is constructed, and the temporal-dynamic QoS and flexible capacity of FMS are modeled. Subsequently, we develop a CMfg-SCCCS optimization model aimed at three objectives, along with a cloud-edge collaboration scheduling mechanism to harmonize cloud and edge-local tasks. Finally, a multi-population co-evolution algorithm with adaptive meta-knowledge transfer mechanism is proposed to solve the complex optimization model. The computational experiments serve to validate the effectiveness of the CMfg-SCCCS method and further reveal the superiority of the co-evolution algorithm in enhancing both the convergence and diversity of the population.

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利用多群体协同进化算法实现柔性制造服务的云边协作组成和调度

云制造服务组成和调度（CMfg-SCS）是云制造的重要流程。柔性制造服务（FMS），如工业机器人提供的服务，被广泛应用于云制造，以提高服务质量和效率。然而，传统的 CMfg-SCS 方法无法有效管理 FMS 固有的时间动态 QoS 和灵活能力。为了克服这些挑战，我们提出了一种适用于 FMS 的新型云制造服务云边缘协作合成与调度（CMfg-SCCCS）方法。首先，我们构建了服务-任务匹配超网络，并对 FMS 的时间动态 QoS 和弹性能力进行了建模。随后，我们针对三个目标建立了 CMfg-SCCCS 优化模型，并建立了云-边缘协作调度机制，以协调云任务和边缘本地任务。最后，我们提出了一种具有自适应元知识转移机制的多群体共同进化算法，以解决复杂的优化模型。计算实验验证了 CMfg-SCCCS 方法的有效性，并进一步揭示了协同进化算法在提高种群收敛性和多样性方面的优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.