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. 2023 Apr 14;21(2):24. doi: 10.1007/s10723-023-09660-3

Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource Scheduling Optimization

Yibo Han 1, Pu Han 2, Bo Yuan 3, Zheng Zhang 4,✉, Lu Liu 3, John Panneerselvam 3
PMCID: PMC10103021  PMID: 37089625

Abstract

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

Keywords: Resource scheduling, Vague set theory, Particle swarm optimization algorithm, Model, Multi objective

Author Contributions

Conceptualization, Yibo Han and Pu Han; methodology, Bo Yuan; software, Lu Liu; validation, John Panneerselvam, Zheng Zhang and Yibo Han; formal analysis, Pu Han; investigation,Bo Yuan; resources, Zheng Zhang; data curation, Lu Liu; writing—original draft preparation, John Panneerselvam; writing—review and editing, Yibo Han; visualization, Pu Han; supervision, Bo Yuan; project administration, Zheng Zhang; funding acquisition, Lu Liu All authors have read and agreed to the published version of the manuscript.

Data Availability

The experimental data used to support the findings of this study are available from the corresponding author upon request.

Declarations

Competing interests

The authors declare no competing interests.

Conflicts of Interest

The authors declared that they have no conflicts of interest regarding this work.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yibo Han, Email: hanyibo@nyist.edu.cn.

Pu Han, Email: hanpu@nyist.edu.cn.

Bo Yuan, Email: b.yuan@leicester.ac.uk.

Zheng Zhang, Email: zhangzheng@nyist.edu.cn.

Lu Liu, Email: l.liu@leicester.ac.uk.

John Panneerselvam, Email: j.Panneerselvam@leicester.ac.uk.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The experimental data used to support the findings of this study are available from the corresponding author upon request.

Articles from Journal of Grid Computing are provided here courtesy of Nature Publishing Group

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