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Performance assessment of airlines using range-adjusted measure, strong complementary slackness condition, and discriminant analysis

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  • Tavassoli, Mohammad
  • Badizadeh, Taliva
  • Farzipoor Saen, Reza

Abstract

This study integrates RAM (range-adjusted measure), SCSC (strong complementary slackness condition), and DEA–DA (data envelopment analysis–discriminant analysis) to rank airlines. As conventional DEA models do not fully use all inputs and outputs, they result zero in many multipliers. These sorts of DEA models may yield many efficient decision-making units (DMUs). This decreases the discrimination power of DEA. To overcome this limitation, this study proposes a novel application of RAM–DEA/SCSC along with DA. A case study demonstrates the applicability of our proposed approach.

Suggested Citation

  • Tavassoli, Mohammad & Badizadeh, Taliva & Farzipoor Saen, Reza, 2016. "Performance assessment of airlines using range-adjusted measure, strong complementary slackness condition, and discriminant analysis," Journal of Air Transport Management, Elsevier, vol. 54(C), pages 42-46.
  • Handle: RePEc:eee:jaitra:v:54:y:2016:i:c:p:42-46
    DOI: 10.1016/j.jairtraman.2016.02.014
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    References listed on IDEAS

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    1. Reza Farzipoor Saen, 2013. "Using cluster analysis and DEA-discriminant analysis to predict group membership of new customers," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 6(3), pages 348-360.
    2. Sueyoshi, Toshiyuki & Sekitani, Kazuyuki, 2007. "Measurement of returns to scale using a non-radial DEA model: A range-adjusted measure approach," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1918-1946, February.
    3. William Cooper & Kyung Park & Jesus Pastor, 1999. "RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models, and Relations to Other Models and Measures in DEA," Journal of Productivity Analysis, Springer, vol. 11(1), pages 5-42, February.
    4. Rico Merkert & James Pearson, 2015. "A Non-parametric Efficiency Measure Incorporating Perceived Airline Service Levels and Profitability," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 261-275, April.
    5. Sueyoshi, Toshiyuki, 1999. "DEA-discriminant analysis in the view of goal programming," European Journal of Operational Research, Elsevier, vol. 115(3), pages 564-582, June.
    6. Barros, Carlos Pestana & Wanke, Peter, 2015. "An analysis of African airlines efficiency with two-stage TOPSIS and neural networks," Journal of Air Transport Management, Elsevier, vol. 44, pages 90-102.
    7. Wanke, Peter & Pestana Barros, Carlos & Chen, Zhongfei, 2015. "An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models," International Journal of Production Economics, Elsevier, vol. 169(C), pages 110-126.
    8. Sueyoshi, Toshiyuki & Goto, Mika, 2011. "Measurement of Returns to Scale and Damages to Scale for DEA-based operational and environmental assessment: How to manage desirable (good) and undesirable (bad) outputs?," European Journal of Operational Research, Elsevier, vol. 211(1), pages 76-89, May.
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    Cited by:

    1. Chen Chunhua & Liu Haohua & Tang Lijun & Ren Jianwei, 2021. "A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs," Journal of Systems Science and Information, De Gruyter, vol. 9(4), pages 378-398, August.

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