Abstract

Cloud manufacturing (CMfg) is a new service-oriented manufacturing paradigm in which shared resources are integrated and encapsulated as manufacturing services. When a single service is not able to meet some manufacturing requirement, a composition of multiple services is then required via CMfg. Service composition and optimal selection (SCOS) is a key technique for creating an on-demand quality of service (QoS)-optimal efficient manufacturing service composition to satisfy various user requirements. Given the number of services with the same functionality and a similar level of QoS, SCOS has been seen as a key challenge in CMfg research. One effective approach to solving SCOS problems is to use service domain features (SDF) through investigating the probability of services being used for a specific requirement from multiple perspectives. The approach can result in a division of the service space and then help streamline the service space with large-scale candidate services. The approach can also search for optimal subspaces that most likely contribute to an overall optimal solution. Accordingly, this paper develops an SDF-oriented genetic algorithm to effectively create a manufacturing service composition with large-scale candidate services. Fine-grained SDF definitions are developed to divide the service space. SDF-based optimization strategies are adopted. The novelty of the proposed algorithm is presented based on Bayes’ theorem. The effectiveness of the proposed algorithm is validated by solving three real-world SCOS problems in a private CMfg.

中文翻译：

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SDF-GA：用于制造云服务组合的面向服务域特征的方法

摘要

云制造（CMfg）是一种面向服务的新型制造范例，其中共享资源被集成并封装为制造服务。当单个服务不能满足某些制造要求时，则需要通过CMfg组合多个服务。服务组合和最佳选择（SCOS）是创建按需服务质量（QoS）的最佳有效制造服务组合以满足各种用户需求的关键技术。鉴于具有相同功能和类似QoS水平的服务数量，SCOS被视为CMfg研究中的关键挑战。解决SCOS问题的一种有效方法是通过从多个角度调查服务用于特定需求的可能性来使用服务域功能（SDF）。该方法可以导致服务空间的划分，然后通过大规模的候选服务帮助简化服务空间。该方法还可以搜索最有可能有助于整体最优解决方案的最优子空间。因此，本文开发了一种面向SDF的遗传算法，以有效地创建具有大规模候选服务的制造服务组合。开发了细粒度的SDF定义以划分服务空间。采用基于SDF的优化策略。基于贝叶斯定理，提出了该算法的新颖性。通过在私有CMfg中解决三个现实世界中的SCOS问题，验证了所提算法的有效性。该方法还可以搜索最有可能有助于整体最优解决方案的最优子空间。因此，本文开发了一种面向SDF的遗传算法，以有效地创建具有大规模候选服务的制造服务组合。开发了细粒度的SDF定义以划分服务空间。采用基于SDF的优化策略。基于贝叶斯定理，提出了该算法的新颖性。通过在私有CMfg中解决三个现实世界中的SCOS问题，验证了所提算法的有效性。该方法还可以搜索最有可能有助于整体最优解决方案的最优子空间。因此，本文开发了一种面向SDF的遗传算法，以有效地创建具有大规模候选服务的制造服务组合。开发了细粒度的SDF定义以划分服务空间。采用基于SDF的优化策略。基于贝叶斯定理，提出了该算法的新颖性。通过在私有CMfg中解决三个现实世界中的SCOS问题，验证了所提算法的有效性。开发了细粒度的SDF定义以划分服务空间。采用基于SDF的优化策略。基于贝叶斯定理，提出了该算法的新颖性。通过在私有CMfg中解决三个现实世界中的SCOS问题，验证了所提算法的有效性。开发了细粒度的SDF定义以划分服务空间。采用基于SDF的优化策略。基于贝叶斯定理，提出了该算法的新颖性。通过在私有CMfg中解决三个现实世界中的SCOS问题，验证了所提算法的有效性。