SDF-GA: a service domain feature-oriented approach for manufacturing cloud service composition
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DOI: 10.1007/s10845-019-01472-1
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References listed on IDEAS
- Hong Jin & Xifan Yao & Yong Chen, 2017. "Correlation-aware QoS modeling and manufacturing cloud service composition," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1947-1960, December.
- Hua Guo & Lin Zhang & Yilong Liu & Fei Tao & Min Shu & Shaomin Mu, 2014. "A discovery method of service-correlation for service composition in virtual enterprise," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 8(5), pages 579-618.
- Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
- F. Tao & Y. Cheng & L. Zhang & A. Y. C. Nee, 2017. "Advanced manufacturing systems: socialization characteristics and trends," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1079-1094, June.
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Cited by:
- Reza Vatankhah Barenji, 2022. "A blockchain technology based trust system for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1451-1465, June.
- Hongbin Wang & Yang Ding & Hanchuan Xu, 2024. "Particle swarm optimization service composition algorithm based on prior knowledge," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 35-53, January.
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Keywords
Service domain features (SDFs); Service composition and optimal selection (SCOS); Cloud manufacturing (CMfg); Manufacturing cloud service composition; Genetic algorithm (GA);All these keywords.
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