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An effective adaptive adjustment method for service composition exception handling in cloud manufacturing

Author

Listed:
  • Yankai Wang

    (Chongqing University)

  • Shilong Wang

    (Chongqing University)

  • Bo Yang

    (Chongqing University)

  • Bo Gao

    (Chongqing University)

  • Sibao Wang

    (Chongqing University)

Abstract

With the increasing market features of globalization, service and customization, the way manufacturers conduct manufacturing business is changing. Under this background, Cloud Manufacturing (CMfg) emerges as a new networked manufacturing model. However, CMfg is immature in many aspects, especially in exception handling of service composition execution. Due to the complexity of the enterprise manufacturing process, there are a large number of unpredictable abnormal events in the dynamic open cloud manufacturing environment (such as user demand change, machine failure, etc.), so in order to ensure the smooth implementation of the service combination, it is indispensable to establish an effective service exception handling mechanism in CMfg. Moreover, when an exception occurs, in order to ensure the smooth execution of the downstream services after the exception point, the exception handling must satisfy the strict time constraints. To realize the exception-handing of service-composition with the strict deadline or strict time constraints, this paper proposes a service-composition exception adaptive adjustment model, considering the influences of the logistics transferring time and cost. And the occupied time of the cloud services and the valid replacement time range of the exception service are considered as the constraints in this model. In addition, the processing quality, the cost, and the quality of service are set as the optimal objectives. On the above basis, a service-composition exception handling adaptive adjustment (SCEHAA) algorithm based on the improved ant colony optimization algorithm (ACO) is proposed and applied to address the above model. Finally, to validate the performance of SCEHAA, a case study and the comparison experiment between SCEHAA and other algorithms (Particle Swarm Optimization and Artificial Bee Colony) are performed. The results show that the SCEHAA algorithm can perform the adaptive adjustment of the service-composition with strict time limit effectively, through the adaptive service execution path reconfiguration and has fast convergence effects.

Suggested Citation

  • Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01652-4
    DOI: 10.1007/s10845-020-01652-4
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    References listed on IDEAS

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    1. W. Qin & J. Zhang & D. Song, 2018. "An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 891-904, April.
    2. Li-Nan Zhu & Peng-Hang Li & Xiao-Long Zhou, 2019. "IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing," Complexity, Hindawi, vol. 2019, pages 1-21, February.
    3. 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.
    4. Yang-Kuei Lin & Chin Soon Chong, 2017. "Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1189-1201, June.
    5. Yingfeng Zhang & Geng Zhang & Yang Liu & Di Hu, 2017. "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1109-1123, June.
    6. 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.
    7. Yabo Luo, 2017. "Nested optimization method combining complex method and ant colony optimization to solve JSSP with complex associated processes," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1801-1815, December.
    8. Chunyang Yu & Wei Zhang & Xun Xu & Yangjian Ji & Shiqiang Yu, 2018. "Data mining based multi-level aggregate service planning for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1351-1361, August.
    9. Malik Khalfallah & Nicolas Figay & Catarina Ferreira Da Silva & Parisa Ghodous, 2016. "A cloud-based platform to ensure interoperability in aerospace industry," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 119-129, February.
    10. Octavian Morariu & Cristina Morariu & Theodor Borangiu, 2016. "Shop-floor resource virtualization layer with private cloud support," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 447-462, April.
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    Cited by:

    1. Zhitao Xu & Adel Elomri & Roberto Baldacci & Laoucine Kerbache & Zhenyong Wu, 2024. "Frontiers and trends of supply chain optimization in the age of industry 4.0: an operations research perspective," Annals of Operations Research, Springer, vol. 338(2), pages 1359-1401, July.
    2. 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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