Abstract With the Internet of Things, it is now possible to sense the real-time status of manufacturing objects and processes. For complex Service Selection (SS) in Cloud Manufacturing, real-time information can be utilized to deal with uncertainties emerging during task execution. Moreover, in the face of diversified demands, multiple manufacturing clouds (MCs) can provide a much wider range of choices of services with their real-time status. However, most researchers have neglected the superiority of multiple MCs and failed to make a study of how to utilize the abundant and diverse resources of multiple MCs, let alone the multi-MCs service mode under dynamic environment. Therefore, we first propose a new dynamic SS paradigm that can leverage the abundant services from multiple MCs, real-time sensing ability of the Internet of Things (IoT) and big data analytics technology for knowledge and insights. In this way, providing optimal manufacturing services (with high QoS) for customers can be guaranteed under dynamic environments. In addition, considering that a relatively long time might be spent to complete a complex manufacturing task after SS, a quantified approach, based on the Analytic Hierarchy Process and big data, is proposed to evaluate whether the intended cloud manufacturing services should be reserved to make sure that eligible services are ready to use without compromising cost or time. In this paper, the problem of IoT-enabled dynamic SS across multiple MCs is formulated in detail to enable an event-driven adaptive scheduling when the model is faced with three kinds of uncertainties (of the service market, service execution and the user side respectively). Experiments with different settings are also performed, which show the advantages of our proposed paradigm and optimization model.

中文翻译：

---

在多个制造云中实现基于物联网的动态服务优化选择

摘要 随着物联网，现在可以感知制造对象和过程的实时状态。对于云制造中复杂的服务选择 (SS)，可以利用实时信息来处理任务执行过程中出现的不确定性。此外，面对多样化的需求，多个制造云（MC）可以根据其实时状态提供更广泛的服务选择。然而，大多数研究人员忽视了多MC的优势，未能研究如何利用多MC丰富多样的资源，更不用说动态环境下的多MC服务模式了。因此，我们首先提出了一种新的动态 SS 范式，可以利用来自多个 MC 的丰富服务，物联网 (IoT) 的实时感知能力和大数据分析技术的知识和洞察力。这样，可以保证在动态环境下为客户提供最优的制造服务（具有高 QoS）。此外，考虑到SS后完成一项复杂的制造任务可能需要相对较长的时间，因此提出了一种基于层次分析过程和大数据的量化方法来评估是否应该保留预期的云制造服务来使确保符合条件的服务可以随时使用，而不会影响成本或时间。在本文中，详细阐述了跨多个 MC 的 IoT 启用动态 SS 问题，以在模型面临三种不确定性（服务市场、服务执行和用户端）。还进行了不同设置的实验，这显示了我们提出的范式和优化模型的优势。