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Distributed manufacturing resource selection strategy in cloud manufacturing

  • Lei Wang
  • Shunsheng Guo
  • Xixing Li
  • Baigang Du
  • Wenxiang Xu
ORIGINAL ARTICLE

Abstract

With the development of the information technology and logistics industry, industrial production models are more likely to be innovated than ever before. Therefore, there is a tendency for a large number of manufacturing enterprises to start outsourcing their manufacturing activities to more professional subcontractors so they could pay more attention to their core business. Cloud manufacturing (CMfg), as a supplement to cloud computing and big data, is also a new network manufacturing mode that is service-oriented. This mode makes it even more complex and impractical to organize and optimize manufacturing resources. Considering this problem, this paper proposes a manufacturing resource selection strategy based on an improved distributed genetic algorithm (DGA) for manufacturing resource combinatorial optimization (MRCO) in CMfg. We divided the DGA into several sections and distributed and optimized the process, which not only guaranteed algorithm speed but also expanded the search range and improved the accuracy. A case study, a performance comparison between a simple genetic algorithm (SGA) and a working procedure priority-based algorithm (WPPBA) is presented later in this paper. Experimental results showed that the proposed method is preferable and a more effective choice for searching for the optimal solution.

Keywords

Cloud manufacturing Manufacturing resource combinatorial optimization Distributed genetic algorithm Parallel optimization 

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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Lei Wang
    • 1
  • Shunsheng Guo
    • 1
  • Xixing Li
    • 1
  • Baigang Du
    • 1
  • Wenxiang Xu
    • 1
  1. 1.School of Mechanical and Electronic EngineeringWuhan University of TechnologyWuhanChina

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