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Annals of Data Science

Erschienen in:

11.05.2023

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

verfasst von: Yanfen Zhang, Jinyao Ma, Haibin Zhang, Bin Yue

Erschienen in: Annals of Data Science | Ausgabe 5/2023

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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.

Vorheriger Artikel Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS

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Titel: Platform Resource Scheduling Method Based on Branch-and-Bound and Genetic Algorithm
verfasst von: Yanfen Zhang
Jinyao Ma
Haibin Zhang
Bin Yue
Publikationsdatum: 11.05.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science / Ausgabe 5/2023
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-023-00470-8

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