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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

First Page

732

Revised Date

2020-01-15

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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