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A data envelopment analysis model with discretionary and non-discretionary factors in fuzzy environments

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  • Saber Saati
  • Adel Hatami-Marbini
  • Madjid Tavana

Abstract

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evaluation process, input and output data in real-world problems are often imprecise or ambiguous. In addition, real-world problems may also include non-discretionary factors that are beyond the control of a DMU's management. Fuzzy logic and fuzzy sets are widely used to represent ambiguous, uncertain or imprecise data in DEA by formalising the inaccuracies inherent in human decision-making. In this paper, we show that considering bounded factors in DEA models results in a disregard to the concept of relative efficiency since the efficiency of the DMUs are calculated by comparing the DMUs with their lower and/or upper bounds. In addition, we present a fuzzy DEA model with discretionary and non discretionary factors in both the input and output-oriented CCR models. A numerical example is used to demonstrate the applicability and the efficacy of the proposed models.

Suggested Citation

  • Saber Saati & Adel Hatami-Marbini & Madjid Tavana, 2011. "A data envelopment analysis model with discretionary and non-discretionary factors in fuzzy environments," International Journal of Productivity and Quality Management, Inderscience Enterprises Ltd, vol. 8(1), pages 45-63.
    • Handle: RePEc:ids:ijpqma:v:8:y:2011:i:1:p:45-63
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    Citations

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    Cited by:

    1. Mushtaq Taleb & Ruzelan Khalid & Ali Emrouznejad & Razamin Ramli, 2023. "Environmental efficiency under weak disposability: an improved super efficiency data envelopment analysis model with application for assessment of port operations considering NetZero," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6627-6656, July.
    2. Annageldy Arazmuradov, 2016. "Economic prospect on carbon emissions in Commonwealth of Independent States," Economic Change and Restructuring, Springer, vol. 49(4), pages 395-427, November.
    3. Teirlinck, Peter & Khoshnevis, Pegah, 2020. "Within-cluster determinants of output efficiency of R&D in the space industry," Omega, Elsevier, vol. 94(C).
    4. Fernández, David & Pozo, Carlos & Folgado, Rubén & Jiménez, Laureano & Guillén-Gosálbez, Gonzalo, 2018. "Productivity and energy efficiency assessment of existing industrial gases facilities via data envelopment analysis and the Malmquist index," Applied Energy, Elsevier, vol. 212(C), pages 1563-1577.
    5. HATAMI-MARBINI, Adel & AGRELL, Per & AGHAYI, Nazila, 2013. "Imprecise data envelopment analysis for the two-stage process," LIDAM Discussion Papers CORE 2013004, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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