A novel inverse data envelopment analysis model with negative ratio data

Soltanifar M.; Tavana M.; Charles V.; Ghiyasi M.; Sharafi H. · Operational Research (2025) · DOI: 10.1007/s12351-024-00891-0

Resumen

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. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

Instituciones: Pontificia Universidad Católica del Perú