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Using machine learning for service candidate sets retrieval in service composition of cloud-based manufacturing

Abstract

Cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm that is striving to produce highly customized products via sharing resources of multiple manufacturing providers. Distributed nature of this paradigm calls for addressing the service composition problem in order to achieve an optimal status in regard to such a collaboration. However, in the majority of research studies done on service composition in CMfg, the sets of candidate services on which the optimal composition is supposed to be conducted are assumed to be predefined. This is an extreme simplification and does not satisfy the actual requirements of cloud manufacturing. This study is aiming to propose a novel approach that first uses TF-IDF (term frequency-inverse document frequency) vectors extracted from the manufacturing capability data and machine learning algorithms to retrieve candidate sets for each corresponding subtask. Then, the optimal composite service is obtained for each scenario by using metaheuristic algorithms. The results prove the efficacy of this method in resulting in a more comprehensive approach for tackling the service composition problem in cloud manufacturing paradigm.

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The raw/processed data required to reproduce these findings cannot be shared at this time due to technical or time limitations.

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Contributions

Hamed Bouzary carried out the simulations, numerical analysis and respective data analysis, and writing. F. Frank Chen has conducted the investigation, supervision, reviewing and editing.

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Correspondence to F. Frank Chen.

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Cite this article

Bouzary, H., Chen, F.F. & Shahin, M. Using machine learning for service candidate sets retrieval in service composition of cloud-based manufacturing. Int J Adv Manuf Technol 115, 941–948 (2021). https://doi.org/10.1007/s00170-020-06381-9

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Keywords

  • Cloud manufacturing
  • Service composition
  • Candidate sets retrieval
  • Machine learning