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

Tianyang Li, He Ting, Zhongjie Wang, Yufeng Zhang

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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 efficient manufacturing service composition to create an on-demand Quality of Service (QoS) 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 challenging research area in CMfg. One effective approach for solving SCOS problems is to use Service Domain Features (SDF) through investigating the probability of services being used for a specific requirement from multiple angles. The approach can result in a division of service space, and then help narrow down 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. In doing so, 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 Bayesian theorem. The effectiveness of the proposed algorithm is validated through solving three real-world SCOS problems in a private CMfg.
Original languageEnglish
Number of pages25
JournalJournal of Intelligent Manufacturing
Publication statusPublished - Mar 2019


  • Service domain features
  • Service composition and optimal selection
  • Cloud manufacturing
  • manufacturing cloud service composition
  • genetic algorithm


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