Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

从几个功能相当的服务中选择最有效的服务仍然是一个持续的挑战。大多数制造服务选择方法都将静态服务质量 (QoS) 作为主要的竞争力因素。然而，在复杂的任务过程中，当可变网络环境对QoS性能稳定性有显着影响时，难以实现自适应。因此，动态时间 QoS 值而不是固定值正在获得服务评估的基础。当服务需求者选择个性化服务时，用户偏好起着重要作用，而对于时间 QoS 感知云制造 (CMfg) 服务选择方法，这方面的研究很少。此外，获取所有时间 QoS 值是不切实际的，这会影响评估的有效性。所以，本文提出了一种时间感知 CMfg 服务选择方法来解决这些问题。所提出的方法首先通过利用来自时间 QoS 值的相似性特征来开发未知 QoS 预测模型。该模型综合考虑 QoS 属性和服务候选，有助于准确、轻松地预测多维 QoS 值。然后使用可以自适应用户偏好的建议的时间 QoS 测量算法评估整体 QoS。具体来说，我们采用时间 QoS 冲突特征来克服单方面的用户偏好，这在很大程度上被忽视了。实验结果证实，所提出的方法优于经典的时间序列预测方法，并且还可以通过减少用户偏好误判来找到更好的服务。