Abstract
Industrial Internet-of-Things brings cloud and edge resources together to support customized manufacturing. With cloud-edge collaboration, large-scale computational tasks of product and process simulation, force and torque analysis, real-time process control, and so forth, are to be executed in cloud or edge resources, while related manufacturing tasks are to be executed in distributed end devices simultaneously. In this circumstance, hybrid task scheduling becomes a key to implement efficient and intelligent manufacturing. In this paper, a multi-indicator-assisted dynamic Bees Algorithm (MIDBA) is presented to solve large-scale task scheduling problem for cloud-edge collaborative manufacturing. The operators of the Bees Algorithm are modified according to multiple indicators to find suitable cloud-edge collaborative modes, cloud and edge resources. A parallel search scheme is also designed to accelerate the scheduling process for large-scale tasks. We implement numerical studies to examine the proposed algorithm on this problem. Compared to the state-of-the-art algorithms, the parallel MIDBA can find better solutions with lesser time.
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Acknowledgements
This work is supported by the National Key Research and Development Program of China (Grant No. 2018YFB1700603).
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Li, Y., Peng, C., Laili, Y., Zhang, L. (2023). A Parallel Multi-indicator-Assisted Dynamic Bees Algorithm for Cloud-Edge Collaborative Manufacturing Task Scheduling. In: Pham, D.T., Hartono, N. (eds) Intelligent Production and Manufacturing Optimisation—The Bees Algorithm Approach. Springer Series in Advanced Manufacturing. Springer, Cham. https://doi.org/10.1007/978-3-031-14537-7_15
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DOI: https://doi.org/10.1007/978-3-031-14537-7_15
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