Journal of System Simulation
Abstract
Abstract: Aiming at resolving the faults quickly and effectively with limited human resource,an IT maintenance service model with the consideration of service level is proposed, A knowledge model is proposed,a variety of mutation operators and crossover operators are designed. and the improved genetic algorithm (IGA), the intelligent genetic algorithm based on knowledge model (KIGA) and the adaptive intelligent genetic algorithm based on knowledge model (KAIGA) are formed. The results show that the adaptive mutation and crossover probability can accelerate the convergence speed of the solution, and the knowledge model can also improve the optimization effect of the solution
Recommended Citation
Chen, Ruiying; Wang, Chengtao; and Liu, Zhenyuan
(2021)
"Intelligent Genetic Algorithm for Workforce Scheduling Considering Service Level in IT Maintenance Service,"
Journal of System Simulation: Vol. 33:
Iss.
3, Article 22.
DOI: 10.16182/j.issn1004731x.joss.19-0576
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss3/22
First Page
732
Revised Date
2020-01-15
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0576
Last Page
744
CLC
TP391
Recommended Citation
Chen Ruiying, Wang Chengtao, Liu Zhenyuan. Intelligent Genetic Algorithm for Workforce Scheduling Considering Service Level in IT Maintenance Service[J]. Journal of System Simulation, 2021, 33(3): 732-744.
DOI
10.16182/j.issn1004731x.joss.19-0576
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