PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

This research aims to analyse, evaluate and rank the maintenance strategy practised by the train operating companies, specifically by the rolling stock maintenance team. A quantitative method was adopted for data collection. A total of five train operating companies were chosen to participate in a survey that has been carefully designed. The research first identified the maintenance strategy associated with the rolling stock maintenance through systematic literature reviews. Afterwards, six maintenance strategies adopted by the companies were identified. The type of maintenance strategies identified was used to structure the survey questionnaire. Judgemental sampling was utilised for sampling purposes. Finally, the data collected from the survey were analysed using an importance index to complete the ranking analysis. The research discovered that corrective and preventive maintenance strategies are the most commonly adopted among the five Malaysian train operating companies. This study also highlighted the factors that future studies should consider to establish predictive cost models for rolling stock maintenance.

• Albrice, D. (2019). Maintenance optimization model. Retrieved June 9, 2019, from http://www.assetinsights.net/Glossary/G_Maintenance_Optimization_Model.html

• Ali, A. S., Kamaruzzaman, S. N., Sulaiman, R., & Peng, Y. C. (2010). Factors affecting housing maintenance cost in Malaysia. Journal of Facilities Management, 8(4), 285-298. https://doi.org/10.1108/14725961011078990

• Cheng, Y. H., & Tsao, H. L. (2010). Rolling stock maintenance strategy selection, spares parts’ estimation, and replacements’ interval calculation. International Journal of Production Economics, 128(1), 404-412. https://doi.org/10.1016/j.ijpe.2010.07.038

• de Almeida Costa, M., de Azevedo Peixoto Braga, J. P., & Andrade, A. R. (2020). A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process. Quality and Reliability Engineering International, 37(1), 176-198. https://doi.org/10.1002/qre.2729

• de Jonge, B. (2017). Maintenance optimization based on mathematical modeling (Doctoral dissertation). University of Groningen, Netherlands.

• Eisenberger, D., & Fink, O. (2017). Assessment of maintenance strategies for railway vehicles using Petri-nets. Transportation Research Procedia, 27, 205-214. https://doi.org/https://doi.org/10.1016/j.trpro.2017.12.012

• El‐Haram, M. A., & Horner, M. W. (2002). Factors affecting housing maintenance cost. Journal of Quality in Maintenance Engineering, 8(2), 115-123. https://doi.org/10.1108/13552510210430008

• Heizer, J., Render, B., & Munson, C. (2015). Operations management: Sustainability and supply chain management. Pearson.

• Idris, M. F. M., & Saad, N. H. (2020). Mid-life refurbishment maintenance strategy to sustain performance and reliability of train system. Applied Mechanics and Materials, 899, 238-252. https://doi.org/10.4028/www.scientific.net/AMM.899.238

• Jun, H. B., & Shin, J. H. (2015). On condition based maintenance policy. Journal of Computational Design and Engineering, 2(2), 119-127. https://doi.org/10.1016/j.jcde.2014.12.006

• Kalathas, I., & Papoutsidakis, M. (2021). Predictive maintenance using machine learning and data mining: A pioneer method implemented to greek railways. Designs, 5(1), Article 5. https://doi.org/10.3390/designs5010005

• Kwansup, L., Jaechan, L., & Ilhwan, K. (2016, September 27-28). A study on strategy of condition based maintenance for Korea metro rolling stocks. In 7th IET Conference on Railway Condition Monitoring 2016 (RCM 2016). Birmingham, UK.

• Kya, L. T., Ngor, P. Y., & Awang, Z. (2015). Statistics for UiTM 4th edition. Oxford Fajar Sdn. Bhd.

• Lai, Y. C., Fan, D. C., & Huang, K. L. (2015). Optimizing rolling stock assignment and maintenance plan for passenger railway operations. Computers & Industrial Engineering, 85, 284-295. https://doi.org/10.1016/j.cie.2015.03.016

• Lin, B., Wu, J., Lin, R., Wang, J., Wang, H., & Zhang, X. (2019). Optimization of high-level preventive maintenance scheduling for high-speed trains. Reliability Engineering & System Safety, 183, 261-275. https://doi.org/10.1016/j.ress.2018.11.028

• Loubinoux, J. P., Angoiti, I. B. D., Cau, G., Leboeuf, M., Picq, O., Bargellini, G., & Domínguez, M. L. (2013). UIC peer review of operating & maintenance costs of the California high-speed rail project. International Union of Railways.

• Méchain, M., Andrade, A. R., & Gomes, M. C. (2020). Planning maintenance actions in train operating companies - A Portuguese case study. In A. Ball, L. Gelman & B. K. N. Rao (Eds.), Advances in asset management and condition monitoring (pp. 1163-1181). Springer. https://doi.org/10.1007/978-3-030-57745-2_96

• Misra, K. B. (2008). Maintenance engineering and maintainability: An introduction. In Handbook of performability engineering (pp. 755-772). Springer. https://doi.org/10.1007/978-1-84800-131-2_46

• Pun, K. P., Tsang, Y. P., Choy, K. L., Tang, V., & Lam, H. Y. (2017). A Fuzzy-AHP-Based Decision Support System for Maintenance Strategy Selection in Facility Management. In 2017 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 1-7). IEEE Publishing. https://doi.org/10.23919/PICMET.2017.8125300

• Stenström, C., Norrbin, P., Parida, A., & Kumar, U. (2015). Preventive and corrective maintenance - Cost comparison and cost benefit analysis. Structure and Infrastructure Engineering, 12(5), 603-617. https://doi.org/10.1080/15732479.2015.1032983

• Taber, K. S. (2017). The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. https://doi.org/10.1007/s11165-016-9602-2

• Um, I. S., Cheon, H. J., & Lee, H. C. (2011). A simulation-based optimal design and analysis method for designing a train overhaul maintenance facility. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 225(5), 523-539. https://doi.org/10.1243/09544097jrrt309

• Yokoyama, A. (2015). Innovative changes for maintenance of railway by using ICT - To achieve “smart maintenance”. Procedia CIRP, 38, 24-29. https://doi.org/10.1016/j.procir.2015.07.074