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.

中文翻译：

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基于多群体协同进化算法的柔性制造服务云边协作组合与调度


云制造服务组合和调度（CMfg-SCS）是云制造中的基本流程。柔性制造服务（FMS），例如工业机器人提供的服务，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 方法在有效管理 FMS 固有的时间动态 QoS 和灵活能力方面存在不足。为了克服这些挑战，我们提出了一种新颖的 FMS 云制造服务云边缘协作组合和调度（CMfg-SCCCS）方法。首先，构建服务任务匹配超网络，并对FMS的时间动态QoS和灵活容量进行建模。随后，我们开发了一个针对三个目标的 CMfg-SCCCS 优化模型，以及协调云和边缘本地任务的云边缘协作调度机制。最后，提出了一种具有自适应元知识转移机制的多群体协同进化算法来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增强种群收敛性和多样性方面的优越性。