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A novel inverse data envelopment analysis model with negative ratio data

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Abstract

Data envelopment analysis (DEA) is a mathematical programming method for evaluating the efficiency of a homogeneous set of decision-making units (DMUs) using multiple inputs and outputs. Inverse DEA estimates a DMU’s input (or output) when some or all DMU outputs (or inputs) are changed. Ratio DEA (DEA-R) combines DEA with ratio analysis to handle ratio data. Real-world DEA-R models often involve negative values for the inputs or outputs. This study presents a novel model for solving inverse DEA problems with negative ratio data for the first time. We present a real-life case study to demonstrate the applicability and efficacy of the DEA models proposed in this study.

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Authors and Affiliations

  1. Department of Mathematics, Semnan Branch, Islamic Azad University, Semnan, Iran

    Mehdi Soltanifar

  2. Business Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, USA

    Madjid Tavana

  3. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, Paderborn, Germany

    Madjid Tavana

  4. CENTRUM Católica Graduate Business School, Lima, Peru

    Vincent Charles

  5. Pontifical Catholic University of Peru, Lima, Peru

    Vincent Charles

  6. Faculty of Industrial Engineering and Management Science, Shahrood University of Technology, Shahrood, Iran

    Mojtaba Ghiyasi

  7. Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Hamid Sharafi

Authors
  1. Mehdi Soltanifar
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  2. Madjid Tavana
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  3. Vincent Charles
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  4. Mojtaba Ghiyasi
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  5. Hamid Sharafi
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Correspondence to Madjid Tavana.

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Appendix

Appendix

See Table 7

Table 7 Input–output data
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Soltanifar, M., Tavana, M., Charles, V. et al. A novel inverse data envelopment analysis model with negative ratio data. Oper Res Int J 25, 25 (2025). https://doi.org/10.1007/s12351-024-00891-0

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