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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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    Cited by:

    1. 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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