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

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  • 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.

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  • 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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    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. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. Kourentzes, Nikolaos, 2013. "Intermittent demand forecasts with neural networks," International Journal of Production Economics, Elsevier, vol. 143(1), pages 198-206.
    25. 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.
    26. AfDB AfDB, . "African Development Report 2012 - Overview," African Development Report, African Development Bank, number 464.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. 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.
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    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).

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