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Olumuyiwa, A.O. (2014) A Decision Support Model to Improve Rolling Stock Maintenance Scheduling Based on Reliability and Cost. Master’s Thesis, Stellenbosch University, Stellenbosch.

has been cited by the following article:

• TITLE: Breeding Particle Swarm Optimization for Railways Rolling Stock Preventive Maintenance Scheduling

  AUTHORS: Tarek Aboueldah, Hanan Farag

  KEYWORDS: Railways Rolling Stock, Predictive Maintenance Scheduling Table, Multi Objective Optimization Problem, Breeding Particle Swarm Optimization

  JOURNAL NAME: American Journal of Operations Research, Vol.11 No.5, September 10, 2021

  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.