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Linear robust data envelopment analysis: CCR model with uncertain data

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  • Mahmood Sabouhi
  • Mostafa Mardani

Abstract

Data envelopment analysis (DEA) traditionally assumes that input and output data of the different decision making units (DMUs) are measured with precision. However, in many real applications inputs and outputs are often imprecise. This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimisation approach of Bertsimas and Sim to seek maximisation of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint's protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. Application of the proposed model (LRDEA) to analyse the technical and scale efficiency of potato production in 23 Iranian provinces demonstrates the reliability and flexibility of the model.

Suggested Citation

  • Mahmood Sabouhi & Mostafa Mardani, 2017. "Linear robust data envelopment analysis: CCR model with uncertain data," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 22(2), pages 262-280.
    • Handle: RePEc:ids:ijpqma:v:22:y:2017:i:2:p:262-280
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    Citations

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    Cited by:

    1. Majid Mohammed Kunambi & Hongxing Zheng, 2024. "Contextual Comparative Analysis of Dar es Salaam and Mombasa Port Performance by Using a Hybrid DEA(CVA) Model," Logistics, MDPI, vol. 8(1), pages 1-20, January.
    2. Mostafa Mardani Najafabadi & Niloofar Ashktorab, 2023. "Mathematical programming approaches for modeling a sustainable cropping pattern under uncertainty: a case study in Southern Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9731-9755, September.
    3. Pejman Peykani & Jafar Gheidar-Kheljani & Reza Farzipoor Saen & Emran Mohammadi, 2022. "Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data," Operational Research, Springer, vol. 22(5), pages 5529-5567, November.
    4. Bello, S. & Onolemhemhen, R., 2024. "Does carbon pricing policy influence carbon emission intensity? New Evidence from Canadian Provinces," Cambridge Working Papers in Economics 2445, Faculty of Economics, University of Cambridge.
    5. Mostafa Mardani Najafabadi & Hanieh Kazmi & Somayeh Shirzadi Laskookalayeh & Abas Abdeshahi, 2023. "Investigating the ability of fuzzy and robust DEA models to apply uncertainty conditions: an application for date palm producers," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 776-801, June.

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