SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition

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.