A novel inverse data envelopment analysis model with negative ratio data

Mehdi Soltanifar (), Madjid Tavana (), Vincent Charles (), Mojtaba Ghiyasi () and Hamid Sharafi ()
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Mehdi Soltanifar: Islamic Azad University
Madjid Tavana: La Salle University
Vincent Charles: CENTRUM Católica Graduate Business School
Mojtaba Ghiyasi: Shahrood University of Technology
Hamid Sharafi: Islamic Azad University

Operational Research, 2025, vol. 25, issue 2, No 3, 34 pages

Abstract: 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.

Keywords: Data envelopment analysis; Inverse model; Ratio model; Negative data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12351-024-00891-0

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