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An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing

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Abstract

In cloud manufacturing (CMfg) environments, an increasing number of manufacturing enterprises outsource manufacturing activities to subcontractors that are more professional to focus on the development of their core business. Volume, diversity, and variety are the typical characteristics of outsourcing services for cement equipment manufacturing enterprises (CEMEs). To address the new problem of service discovery and combinatorial optimization of outsourcing resources (COOR), a novel heuristic approach is investigated in this paper. First, a clustering and searching model of a web outsourcing service based on Ontology Web Language for Services (OWL-S) and an artificial neural network (ANN) is established. Then, an improved shuffled frog leaping algorithm (SFLA) is developed to solve the COOR problem. Finally, an investigation and comparative experiments based on a group of cement equipment manufacturing companies is presented. The experimental results show that the proposed method is preferable and is more efficient for solving large-scale problems in a CMfg environment.

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Acknowledgements

This research was supported by the National Nature Science Fund Project of China (No. 51705386), the National Natural Science Foundation of China (No.51705385), the Fundamental Research Funds for the Central Universities (WUT: 2017II27GX) and the Fundamental Research Funds for the Central Universities (WUT: 2017-IVA-016). The authors would like to express their great appreciation for the valuable comments and constructive suggestions made by the anonymous reviewers and the editor.

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Wang, L., Guo, C., Li, Y. et al. An outsourcing service selection method using ANN and SFLA algorithms for cement equipment manufacturing enterprises in cloud manufacturing. J Ambient Intell Human Comput 10, 1065–1079 (2019). https://doi.org/10.1007/s12652-017-0612-3

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