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Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm

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Abstract

Platform resource scheduling is an operational research optimization problem of matching tasks and platform resources, which has important applications in production or marketing arrangement layout, combat task planning, etc. The existing algorithms are inflexible in task planning sequence and have poor stability. Aiming at this defect, the branch-and-bound algorithm is combined with the genetic algorithm in this paper. Branch-and-bound algorithm can adaptively adjust the next task to be planned and calculate a variety of feasible task planning sequences. Genetic algorithm is used to assign a platform combination to the selected task. Besides, we put forward a new lower bound calculation method and pruning rule. On the basis of the processing time of the direct successor tasks, the influence of the resource requirements of tasks on the priority of tasks is considered. Numerical experiments show that the proposed algorithm has good performance in platform resource scheduling problem.

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Data Availability

All case datas are from references, the specific references have been noted in the text. All experimental datas included in the submission.

Code Availability

The Python code in this article is available. If anyone needs this, they can get it by contacting the author.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 11771003).

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Authors and Affiliations

  1. Faculty of Science, Beijing University of Technology, Beijing, 100124, China

    Yanfen Zhang, Jinyao Ma & Haibin Zhang

  2. Beijing Aeronautical Technology Research Center, Beijing, 100076, China

    Bin Yue

Authors
  1. Yanfen Zhang
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  2. Jinyao Ma
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  3. Haibin Zhang
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  4. Bin Yue
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All authors made significant contributions to the preparation and writing of the manuscript.

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Correspondence to Jinyao Ma or Haibin Zhang.

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Zhang, Y., Ma, J., Zhang, H. et al. Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm. Ann. Data. Sci. 10, 1421–1445 (2023). https://doi.org/10.1007/s40745-023-00470-8

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  • DOI: https://doi.org/10.1007/s40745-023-00470-8

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