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Design and Application of Vague Set Theory and Adaptive Grid Particle Swarm Optimization Algorithm in Resource Scheduling Optimization.
Han, Yibo; Han, Pu; Yuan, Bo; Zhang, Zheng; Liu, Lu; Panneerselvam, John.
  • Han Y; Nanyang Institute of Big Data Research, Nanyang Institute of Technology, Nanyang, 473004 China.
  • Han P; School of Information Engineering, Nanyang Institute of Technology, Nanyang, 473004 China.
  • Yuan B; Department of Informatics, University of Leicester, University Rd, Leicester, LE1 7RH UK.
  • Zhang Z; School of Computer and Software, Nanyang Institute of Technology, Nanyang, 473004 China.
  • Liu L; Department of Informatics, University of Leicester, University Rd, Leicester, LE1 7RH UK.
  • Panneerselvam J; Department of Informatics, University of Leicester, University Rd, Leicester, LE1 7RH UK.
J Grid Comput ; 21(2): 24, 2023.
Artículo en Inglés | MEDLINE | ID: covidwho-2308819
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.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Tipo de estudio: Estudio pronóstico Idioma: Inglés Revista: J Grid Comput Año: 2023 Tipo del documento: Artículo

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Tipo de estudio: Estudio pronóstico Idioma: Inglés Revista: J Grid Comput Año: 2023 Tipo del documento: Artículo