Abstract
The goal of railway rolling stock maintenance and replacement approaches is to reduce overall cost while increasing reliability which is multi objective optimization problem and a proper predictive maintenance scheduling table should be adequately designed. We propose Breeding Particle Swarm Optimization (BPSO) model based on the concepts of Breeding Swarm and Genetic Algorithm (GA) operators to design this table. The practical experiment shows that our model reduces cost while increasing reliability compared to other models previously utilized.
Highlights
Railway rolling stock preventive maintenance is usually carried out at predetermined regular time/mileage intervals based on the knowledge and experience of train operating companies, rolling stock owners, original equipment manufacturers [1]
We propose Breeding Particle Swarm Optimization (BPSO) model based on the concepts of Breeding Swarm and Genetic Algorithm (GA) operators to design this table
Railways rolling stocks have been proven to be repairable systems represented by non homogeneous data [5] where Rate of Occurrence of Failures (ROCOF) has a trend and can be predicted by Power Law Non Homogeneous Poisson Process (NHPP) [6]
Summary
Railway rolling stock preventive maintenance is usually carried out at predetermined regular time/mileage intervals based on the knowledge and experience of train operating companies, rolling stock owners, original equipment manufacturers [1]. The goal of railway rolling stock maintenance and replacement approaches is to reduce overall cost while increasing reliability which is multi objective optimization problem and a proper predictive maintenance scheduling table should be adequately designed.
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