IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v183y2017ipap159-170.html
   My bibliography  Save this article

Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling

Author

Listed:
  • Kwon, He-Boong

Abstract

This paper is an investigation into the feasibility of using artificial neural networks (ANN) in conjunction with data envelopment analysis (DEA) for performance measurement and prediction modeling of Class I railroads in the United States. For this exploratory study, DEA-ANN are combined into a two-stage modeling approach. While it is frequently used as a benchmarking tool, DEA lacks predictive capabilities. However, ANN has strong nonlinear mapping and adaptive prediction functionality. In this study, the advantages of combining these complementary methods into an integrated performance measurement and prediction model are explored. For this combined approach, a Charnes, Cooper and Rhodes (CCR) DEA model is used to evaluate the efficiency of each decision making unit (DMU) and to capture the efficiency trend of each railroad. Based upon those DEA results, the follow-on backpropagation neural network (BPNN) model predicts an efficiency score and target output for each DMU. This is a new attempt to extend the BPNN model for purposes of best performance prediction. The resulting framework is an effective benchmarking and decision support system which adds adaptive prediction capabilities to current benchmarking practices.

Suggested Citation

  • Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
  • Handle: RePEc:eee:proeco:v:183:y:2017:i:pa:p:159-170
    DOI: 10.1016/j.ijpe.2016.10.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527316303188
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2016.10.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Abate, Megersa & Lijesen, Mark & Pels, Eric & Roelevelt, Adriaan, 2013. "The impact of reliability on the productivity of railroad companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 51(C), pages 41-49.
    2. Chandra, Pankaj & Cooper, William W. & Li, Shanling & Rahman, Atiqur, 1998. "Using DEA To evaluate 29 Canadian textile companies -- Considering returns to scale," International Journal of Production Economics, Elsevier, vol. 54(2), pages 129-141, January.
    3. Cavalieri, Sergio & Maccarrone, Paolo & Pinto, Roberto, 2004. "Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(2), pages 165-177, September.
    4. Antonio Couto & Daniel Graham, 2009. "The determinants of efficiency and productivity in European railways," Applied Economics, Taylor & Francis Journals, vol. 41(22), pages 2827-2851.
    5. Siew Hoon Lim & C. A. Knox Lovell, 2008. "Short-run Total Cost Change and Productivity of US Class I Railroads," Journal of Transport Economics and Policy, University of Bath, vol. 42(1), pages 155-188, January.
    6. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    7. Barros, Carlos P. & Bin Liang, Qi & Peypoch, Nicolas, 2013. "The efficiency of French regional airports: An inverse B-convex analysis," International Journal of Production Economics, Elsevier, vol. 141(2), pages 668-674.
    8. Andreas Andrikopoulos & John Loizides, 1998. "Cost structure and productivity growth in European railway systems," Applied Economics, Taylor & Francis Journals, vol. 30(12), pages 1625-1639.
    9. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    10. AfDB AfDB, . "African Development Report 2012 - Overview," African Development Report, African Development Bank, number 464, August.
    11. You Li & Lu Liu, 2012. "Hybrid artificial neural network and statistical model for forecasting project total duration in earned value management," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 10(3/4), pages 402-413.
    12. Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
    13. Daniela Carlucci & Paolo Renna & Giovanni Schiuma, 2013. "Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network," Health Care Management Science, Springer, vol. 16(1), pages 37-44, March.
    14. Daniel Santin, 2008. "On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques," Applied Economics Letters, Taylor & Francis Journals, vol. 15(8), pages 597-600.
    15. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    16. Tim Coelli & Sergio Perelman, 2000. "Technical efficiency of European railways: a distance function approach," Applied Economics, Taylor & Francis Journals, vol. 32(15), pages 1967-1976.
    17. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2013. "Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system," Omega, Elsevier, vol. 41(1), pages 131-142.
    18. Hailin Liao & Bin Wang & Tom Weyman-Jones, 2007. "Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition," Global Economic Review, Taylor & Francis Journals, vol. 36(4), pages 361-384.
    19. Daniel Santin & Francisco Delgado & Aurelia Valino, 2004. "The measurement of technical efficiency: a neural network approach," Applied Economics, Taylor & Francis Journals, vol. 36(6), pages 627-635.
    20. Carlos Pestana Barros & Peter Wanke, 2014. "Insurance companies in Mozambique: a two-stage DEA and neural networks on efficiency and capacity slacks," Applied Economics, Taylor & Francis Journals, vol. 46(29), pages 3591-3600, October.
    21. Siew Hoon Lim & C.A. Knox Lovell, 2009. "Profit and productivity of US Class I railroads," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 30(7), pages 423-442.
    22. Lawrence M. Seiford & Joe Zhu, 1999. "Profitability and Marketability of the Top 55 U.S. Commercial Banks," Management Science, INFORMS, vol. 45(9), pages 1270-1288, September.
    23. Mohamed M. Mostafa, 2009. "A probabilistic neural network approach for modelling and classifying efficiency of GCC banks," International Journal of Business Performance Management, Inderscience Enterprises Ltd, vol. 11(3), pages 236-258.
    24. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    25. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    26. Lucia Parisio, 1999. "A comparative analysis of European railroads efficiency: a cost frontier approach," Applied Economics, Taylor & Francis Journals, vol. 31(7), pages 815-823.
    27. Feli X. Shi & Siew Hoon Lim & Junwook Chi, 2011. "Railroad productivity analysis: case of the American Class I railroads," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 60(4), pages 372-386, April.
    28. Wang, Chun-Hsien & Lu, Yung-Hsiang & Huang, Chin-Wei & Lee, Jun-Yen, 2013. "R&D, productivity, and market value: An empirical study from high-technology firms," Omega, Elsevier, vol. 41(1), pages 143-155.
    29. Fernandes, Elton & Pacheco, R. R., 2002. "Efficient use of airport capacity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(3), pages 225-238, March.
    30. Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
    31. Mary O'Mahony & Nicholas Oulton, 2000. "International Comparisons of Labour Productivity in Transport and Communications: The US, the UK and Germany," Journal of Productivity Analysis, Springer, vol. 14(1), pages 7-30, July.
    32. Ülengin, Füsun & Kabak, Özgür & Önsel, Sule & Aktas, Emel & Parker, Barnett R., 2011. "The competitiveness of nations and implications for human development," Socio-Economic Planning Sciences, Elsevier, vol. 45(1), pages 16-27, March.
    33. John D. Bitzan & Theodore E. Keeler, 2003. "Productivity Growth and Some of Its Determinants in the Deregulated U.S. Railroad Industry," Southern Economic Journal, John Wiley & Sons, vol. 70(2), pages 232-253, October.
    34. Tsai, Hsiang-Chih & Chen, Chun-Mei & Tzeng, Gwo-Hshiung, 2006. "The comparative productivity efficiency for global telecoms," International Journal of Production Economics, Elsevier, vol. 103(2), pages 509-526, October.
    35. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Syyed Adnan Raheel Shah & Naveed Ahmad & Yongjun Shen & Ali Pirdavani & Muhammad Aamir Basheer & Tom Brijs, 2018. "Road Safety Risk Assessment: An Analysis of Transport Policy and Management for Low-, Middle-, and High-Income Asian Countries," Sustainability, MDPI, vol. 10(2), pages 1-30, February.
    2. Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
    3. Bodin Singpai & Desheng Wu, 2020. "Using a DEA–AutoML Approach to Track SDG Achievements," Sustainability, MDPI, vol. 12(23), pages 1-26, December.
    4. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    5. Kwon, He-Boong & Lee, Jooh, 2019. "Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in high-tech-oriented firms," International Journal of Production Economics, Elsevier, vol. 211(C), pages 1-14.
    6. Mansour Zarrin & Jan Schoenfelder & Jens O. Brunner, 2022. "Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework," Health Care Management Science, Springer, vol. 25(3), pages 406-425, September.
    7. Zhou, Xiaoyang & Chen, Hao & Chai, Jian & Wang, Shouyang & Lev, Benjamin, 2020. "Performance evaluation and prediction of the integrated circuit industry in China: A hybrid method," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    8. Xiaohong Yu & Wengao Lou, 2023. "An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regressio," Mathematics, MDPI, vol. 11(23), pages 1-29, November.
    9. Lin, Sin-Jin & Zeng, Jhih-Hong & Chang, Te-Min & Hsu, Ming-Fu, 2024. "Linguistic complexity consideration for advanced risk decision making and handling," Research in International Business and Finance, Elsevier, vol. 69(C).
    10. Kwon, He-Boong & Lee, Jooh & Choi, Laee, 2022. "Dynamic interplay of operations and R&D capabilities in U.S. high-tech firms: Predictive impact analysis," International Journal of Production Economics, Elsevier, vol. 247(C).
    11. He-Boong Kwon & Jooh Lee & Laee Choi, 2023. "Dynamic interplay of environmental sustainability and corporate reputation: a combined parametric and nonparametric approach," Annals of Operations Research, Springer, vol. 324(1), pages 687-719, May.
    12. Yu, Xiaohong & Xu, Haiyan & Lou, Wengao & Xu, Xun & Shi, Victor, 2023. "Examining energy eco-efficiency in China's logistics industry," International Journal of Production Economics, Elsevier, vol. 258(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Jooh & Kwon, He-Boong, 2017. "Progressive performance modeling for the strategic determinants of market value in the high-tech oriented SMEs," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 91-102.
    2. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    3. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    4. Kwon, He-Boong & Lee, Jooh, 2019. "Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in high-tech-oriented firms," International Journal of Production Economics, Elsevier, vol. 211(C), pages 1-14.
    5. Lu, Wen-Min & Liu, John S. & Kweh, Qian Long & Wang, Chung-Wei, 2016. "Exploring the benchmarks of the Taiwanese investment trust corporations: Management and investment efficiency perspectives," European Journal of Operational Research, Elsevier, vol. 248(2), pages 607-618.
    6. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    7. Kremantzis, Marios Dominikos & Beullens, Patrick & Kyrgiakos, Leonidas Sotirios & Klein, Jonathan, 2022. "Measurement and evaluation of multi-function parallel network hierarchical DEA systems," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    8. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    9. Chiang Kao & Shiang-Tai Liu, 2022. "Stochastic efficiencies of network production systems with correlated stochastic data: the case of Taiwanese commercial banks," Annals of Operations Research, Springer, vol. 315(2), pages 1151-1174, August.
    10. Huang, Chin-wei & Ho, Foo Nin & Chiu, Yung-ho, 2014. "Measurement of tourist hotels׳ productive efficiency, occupancy, and catering service effectiveness using a modified two-stage DEA model in Taiwan," Omega, Elsevier, vol. 48(C), pages 49-59.
    11. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    12. Bai, Xuejie & Jin, Zeng & Chiu, Yung-Ho, 2021. "Performance evaluation of China's railway passenger transportation sector," Research in Transportation Economics, Elsevier, vol. 90(C).
    13. Mohammad Nourani & Qian Long Kweh & Wen-Min Lu & Ikhlaas Gurrib, 2022. "Operational and investment efficiency of investment trust companies: Do foreign firms outperform domestic firms?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-26, December.
    14. Huang, Chin-wei & Chiu, Yung-ho & Fang, Wei-ta & Shen, Neng, 2014. "Assessing the performance of Taiwan’s environmental protection system with a non-radial network DEA approach," Energy Policy, Elsevier, vol. 74(C), pages 547-556.
    15. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    16. Huang, Chin-wei, 2018. "Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model," Tourism Management, Elsevier, vol. 65(C), pages 303-316.
    17. Chu, Junfei & Zhu, Joe, 2021. "Production scale-based two-stage network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 294(1), pages 283-294.
    18. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    19. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    20. Cavaignac, Laurent & Petiot, Romain, 2017. "A quarter century of Data Envelopment Analysis applied to the transport sector: A bibliometric analysis," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 84-96.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:183:y:2017:i:pa:p:159-170. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.