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A novel inverse data envelopment analysis model with negative ratio data

Author

Listed:
  • Mehdi Soltanifar

    (Islamic Azad University)

  • Madjid Tavana

    (La Salle University
    University of Paderborn)

  • Vincent Charles

    (CENTRUM Católica Graduate Business School
    Pontifical Catholic University of Peru)

  • Mojtaba Ghiyasi

    (Shahrood University of Technology)

  • Hamid Sharafi

    (Islamic Azad University)

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.

Suggested Citation

  • Mehdi Soltanifar & Madjid Tavana & Vincent Charles & Mojtaba Ghiyasi & Hamid Sharafi, 2025. "A novel inverse data envelopment analysis model with negative ratio data," Operational Research, Springer, vol. 25(2), pages 1-34, June.
    • Handle: RePEc:spr:operea:v:25:y:2025:i:2:d:10.1007_s12351-024-00891-0
    DOI: 10.1007/s12351-024-00891-0
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