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Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm

Author

Listed:
  • Jorick Lartigau
  • Xiaofei Xu
  • Lanshun Nie
  • Dechen Zhan

Abstract

Cloud Manufacturing (CMfg) ambitions to create dedicated manufacturing clouds (i.e. virtual enterprises) for complex manufacturing demands through the association of various service providers’ resources and capabilities. In order to insure a dedicated manufacturing cloud to match the level of customer’s requirements, the cloud service selection and composition appear to be a decisive process. This study takes common aspects of cloud services into consideration such as quality of service (QoS) parameters but extend the scope to the physical location of the manufacturing resources. Unlike the classic service composition, manufacturing brings additional constraints. Consequently, we propose a method based on QoS evaluation along with the geo-perspective correlation from one cloud service to another for transportation impact analysis. We also insure the veracity of the manufacturing time evaluation by resource availability overtime. Since the composition is an exhaustive process in terms of computational time consumption, the proposed method is optimised through an adapted Artificial Bee Colony (ABC) algorithm based on initialisation enhancement. Finally, the efficiency and precision of our method are discussed furthermore in the experiments chapter.

Suggested Citation

  • Jorick Lartigau & Xiaofei Xu & Lanshun Nie & Dechen Zhan, 2015. "Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 53(14), pages 4380-4404, July.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:14:p:4380-4404
    DOI: 10.1080/00207543.2015.1005765
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    Citations

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    Cited by:

    1. Wei He & Guozhu Jia & Hengshan Zong & Jili Kong, 2019. "Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    2. Yan-chao Yin & Fu-zhao Chen & Wei-zhi Liao & Cui-yin Liu, 2019. "An Optimal Composition Strategy for Knowledge Service Component Based on Flexible Tracking Particle Swarm Algorithm," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    3. Daozhi Zhao & Yang Xue & Cejun Cao & Hongshuai Han, 2019. "Channel Selection and Pricing Decisions Considering Three Charging Modes of Production Capacity Sharing Platform: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(21), pages 1-28, October.
    4. Ali Salmasnia & Zahra Kiapasha & Melika Pashaeenejad, 2024. "Subtasks scheduling of tasks with different structures in cloud manufacturing systems under maintenance policy and focusing on logistics, tardiness, and earliness aspects," Operational Research, Springer, vol. 24(3), pages 1-37, September.
    5. Dong Yang & Qidong Liu & Jia Li & Yongji Jia, 2020. "Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    6. Yingfeng Zhang & Dong Xi & Haidong Yang & Fei Tao & Zhe Wang, 2019. "Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2681-2699, October.
    7. Yang Yu & Ray Qing Cao & Dara Schniederjans, 2017. "Cloud computing and its impact on service level: a multi-agent simulation model," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4341-4353, August.

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