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Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution

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Abstract

Manufacturing systems develop toward cloud-edge collaboration where manufacturing and computation are tightly coupled. Under this circumstance, large-scale hybrid tasks that include manufacturing and computational tasks need to be collaboratively scheduled among heterogeneous resources. This paper solves the hybrid task scheduling problem in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution (F-RDE). First, we establish a system model for hybrid task scheduling with the objective of minimizing the total makespan and energy consumption. This model includes four types of time constraints between the hybrid tasks. The scheduling of such hybrid tasks has received limited attention in existing research. Next, large-scale decision variables are encoded into the evolutionary chromosomes. To generate offspring chromosomes, we construct four differential evolution operators that are randomly selected during the search process. Furthermore, we propose the fully convolutional regression network (FCRN) as a novel surrogate model to accelerate fitness evaluation. To enhance the integration of FCRN and the differential evolution procedure, we employ three strategies: chromosome folding, top-K re-evaluation, and three training modes. The FCRN surrogate can effectively represent chromosomes with up to 12000 dimensions and achieve generalization across diverse scheduling cases. This leads to reduced solving time and enhanced fitness estimation accuracy. Numerical experiments on three hybrid task scheduling cases validate the superiority compared to the other twelve scheduling algorithms, and the proposed FCRN surrogate can save at most 43% of solving time.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant Nos.62173017 and 61873014). The research is also supported by the international joint doctoral education fund of Beihang University.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Xiaohan Wang, Lin Zhang, Yuanjun Laili & Zhen Chen

  2. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, 10044, Sweden

    Xiaohan Wang

  3. School of Mechano-Electronic Engineering, Xidian University, Xi’an, 710071, China

    Yongkui Liu

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Feng Li

  5. Computer School, Beijing Information Science and Technology University, Beijing, 100101, China

    Chun Zhao

Authors
  1. Xiaohan Wang
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  2. Lin Zhang
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  3. Yuanjun Laili
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  4. Yongkui Liu
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  5. Feng Li
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  7. Chun Zhao
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Contributions

All authors contributed to the study conception and design. Xiaohan Wang: Methodology, Experiments, Writing-Original draft preparation. Lin Zhang: Supervision, Writing-Reviewing and Editing. Yuanjun Laili: Conceptualization, Supervision. Yongkui Liu: Visualization, Investigation. Zhen Chen: Experiments, Investigation. Chun Zhao: Visualization, Data curation. All authors read and approved the final manuscript.

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Correspondence to Lin Zhang or Yuanjun Laili.

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Wang, X., Zhang, L., Laili, Y. et al. Large-scale hybrid task scheduling in cloud-edge collaborative manufacturing systems with FCRN-assisted random differential evolution. Int J Adv Manuf Technol 130, 203–221 (2024). https://doi.org/10.1007/s00170-023-12595-4

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