Analysis and Improvement of Midterm Maintenance Productivity for High-Speed Trains
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 12
Abstract
Among mass transportation facilities, high-speed rail systems not only connect major cities but they also drastically shorten the transportation time. The high-speed train maintenance works are an important part of the safety operation system. Also, the reasonable planning and maintenance works execution can ensure the safe running of trains, improve the quality of operation, and reduce company costs. The regular maintenance is a critical element for providing an excellent transportation service to the public. The present study uses data envelopment analysis (DEA) and the Malmquist productivity index (MPI) to analyze the multiyear trends in productivity for the yearly and midterm maintenance efficiencies of 12 high-speed trains in Taiwan. These features are evaluated through systems thinking, and suggestions are offered towards improving those factors responsible for the inefficiency. The results show that eight of the trains studied had attained satisfactory maintenance efficiency, whereas four could be improved, yielding a productivity of 66.7%. In addition, by following the trends in the MPI values, it was found that the technical efficiency (TE) and the total factor productivity (TFP) are the main factors responsible for stalling maintenance performance, contributing a decline of 37.1% and 37.7%, respectively. Our study has identified three factors leading to maintenance inefficiency. These three factors are: (1) a change in the scope of the contract, (2) a change of work methods, and (3) a lack of effective communication between the management and the customer. It is hoped that this research can provide useful information that will allow decision-makers to avoid erroneous decisions caused by poor intuitive judgment or inadequate considerations, and to formulate correct policies, to improve maintenance productivity, and to ensure that trains can continue to run safely.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.
Acknowledgments
The authors gratefully acknowledge the financial support of the Ministry of Science and Technology (MOST) under Grant Nos. 106-2221-E-327-016 and 107-2221-E-992-038-MY2.
References
Abdollah, H., and A. F. Ali. 2006. “A generalized DEA model for inputs/outputs estimation.” Math. Comput. Modell. 43 (5–6): 447–457. https://doi.org/10.1016/j.
Anouze, A. L. M., and I. Bou-Hamad. 2019. “Data envelopment analysis and data mining to efficiency estimation and evaluation.” Int. J. Islamic Middle Eastern Finance Manage. 12 (2): 169–190. https://doi.org/10.1108/IMEFM-11-2017-0302.
Azadeh, A., S. F. Ghaderi, M. Mirjalili, and M. Moghaddam. 2011. “Integration of analytic hierarchy process and data envelopment analysis for assessment and optimization of personnel productivity in a large industrial bank.” Expert Syst. Appl. 38 (5): 5212–5225. https://doi.org/10.1016/j.eswa.2010.10.038.
Banker, R. D., A. Charnes, and W. W. Cooper. 1984. “Some models for estimating technical and scale inefficiencies in data envelopment analysis.” Manage. Sci. 30 (9): 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078.
Carter, N. 1991. “Learning to measure performance: The use of indicators in organizations.” Public Administration Rev. 69 (1): 85–101. https://doi.org/10.1111/j.1467-9299.1991.tb00783.x.
Caves, D. W., L. R. Christensen, and W. E. Diewert. 1982. “The economic theory of index number of the measurement of input, output and productivity.” Econometica 50 (6): 1393–1414. https://doi.org/10.2307/1913388.
Charnes, A., W. W. Cooper, and E. Rhodes. 1978. “Measuring the efficiency of decision making units.” Eur. J. Oper. Res. 2 (6): 429–444. https://doi.org/10.1016/0377-2217(78)90138-8.
Eisenberger, D., and O. Fink. 2017. “Assessment of maintenance strategies for railway vehicles using Petri-nets.” Transp. Res. Procedia 27 (Jan): 205–214. https://doi.org/10.1016/j.trpro.2017.12.012.
Färe, R., S. Grosskopt, B. Lindgren, and P. Roos. 1992. “Productivity change in Swedish pharmacies 1980–1989: A nonparametric Malmquist approach.” J. Productivity Anal. 3 (1–2): 85–101. https://doi.org/10.1007/BF00158770.
Färe, R., S. Grosskopt, M. Norris, and Z. Y. Zhang. 1994. “Production growth, technical progress, and efficiency change in industrialized countries.” Am. Econ. Rev. 84 (1): 66–83.
Farrell, M. J. 1957. “The measurement of productive efficiency.” J. R. Stat. Soc. 120 (3): 253–281. https://doi.org/10.2307/2343100.
Felipe, B. M. L., M. P. B. Javier, and R. D. Estember. 2018. “Computerized maintenance management system for the Philippine’s railway transit.” In Proc., Int. Conf. on Industrial Engineering and Operations Management, 2020–2030. Bandung, Indonesia: IEOM Society International.
Fu, Z., G. Wang, F. Gao, X. Tian, Y. Li, and B. Lu. 2012. “Review of high-speed train maintenance.” In Proc., 2012 Int. Conf. on Quality, Reliability, Risk, Maintenance, and Safety Engineering, 419–422. New York: IEEE. https://doi.org/10.1109/ICQR2MSE.2012.6246266.
Golany, B., and Y. Roll. 1989. “An application procedure for DEA.” Omega 17 (3): 237–250. https://doi.org/10.1016/0305-0483(89)90029-7.
Hani, Y., L. Amodeo, F. Yalaoui, and H. Chen. 2008. “Simulation based optimization of a train maintenance facility.” J. Intell. Manuf. 19 (3): 293–300. https://doi.org/10.1007/s10845-008-0082-8.
Ji, Y., and C. Lee. 2010. “Data envelopment analysis.” Stata J.: Promoting Commun. Stat. Stata 10 (2): 267–280. https://doi.org/10.1177/1536867X1001000207.
Kefalidou, G., D. Golightly, and S. Sharples. 2018. “Identifying rail asset maintenance processes: A human-centric and sensemaking approach.” Cognit. Technol. Work 20 (1): 73–92. https://doi.org/10.1007/s10111-017-0452-0.
Kerstens, K. 1996. “Technical efficiency measurement and explanation of French urban transit companies.” Transp. Res. Part A: Policy Pract. 30 (6): 431–452. https://doi.org/10.1016/0965-8564(96)00006-7.
Kim, D. H. 1999. Introduction to systems thinking. Arcadia, CA: Pegasus Communications.
Malmquist, S. 1953. “Index numbers and indifference surfaces.” Trabajos de Estatistica 4 (2): 209–242. https://doi.org/10.1007/BF03006863.
Mason, A. J., D. M. Ryan, and D. M. Panton. 1998. “Integrated simulation, heuristic and optimization approaches to staff scheduling.” Oper. Res. 46 (2): 161–175. https://doi.org/10.1287/opre.46.2.161.
Monfared, R. P., A. A. West, R. Harrison, and S. M. Lee. 2007. “Improving train maintenance through process modelling and component-based system design and implementation.” Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 221 (2): 333–346. https://doi.org/10.1243/09544054JEM433.
Robbins, S. P. 2001. Organization behavior. 6th ed. New York: Prentice Hall.
Rodgers, J. L., and W. A. Nicewander. 1988. “Thirteen ways to look at the correlation coefficient.” Am. Stat. 42 (1): 59–66. https://doi.org/10.2307/2685263.
Sadeghi, J., H. Heydari, and E. A. Doloe. 2017. “Improvement of railway maintenance approach by developing a new railway condition index.” J. Transp. Eng. Part A: Syst. 143 (8): 04017037. https://doi.org/10.1061/JTEPBS.0000063.
Senge, P. M. 2006. The fifth discipline: The art and practice of the learning organization. New York: Currency.
Stuchly, V., J. Grencik, and R. Poprocky. 2000. “Railway vehicle maintenance and information systems.” In Computers in railways VII, edited by C. A. Brebbia, J. Allan, R. J. Hill, G. Sciutto, and S. Sone, 885–894. Billerica, MA: WIT Press.
Weber, C. A. 1996. “A data envelopment analysis approach to measuring vendor performance.” Supply Chain Manage. 1 (1): 28–39. https://doi.org/10.1108/13598549610155242.
Yuan Y., J. Wang, W. ShangGuan, B. Cai, and H. Song. 2019. “Research on identification of maintenance significant items in reliability centered maintenance for train control system.” In Proc., 2019 IEEE Intelligent Transportation Systems Conf. (ITSC), 2817–2822. New York: IEEE. https://doi.org/10.1109/ITSC.2019.8917239.
Zvolensky, P., V. Stuchly, and J. Grencik. 1998. “Computerization of rail vehicles maintenance systems.” Trans. Built Environ. 34 (Aug): 683–692. https://doi.org/10.2495/CR980651.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Apr 8, 2021
Accepted: Aug 23, 2021
Published online: Oct 6, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 6, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Diogo Da Fonseca-Soares, Josicleda Domiciano Galvinicio, Sayonara Andrade Eliziário, Angel Fermin Ramos-Ridao, A Bibliometric Analysis of the Trends and Characteristics of Railway Research, Sustainability, 10.3390/su142113956, 14, 21, (13956), (2022).