Abstract
Data envelopment analysis (DEA) is a non-parametric optimization approach that was first introduced by Charnes et al. (1978) and is widely used for assessing the performance and comparative efficiency of decision-making units (DMUs) in both public and private sectors. It has emerged as a success story of management science and has found applications in various domains, including environmental, banking, healthcare, transportation, education, manufacturing, agriculture, energy, sport, and tourism. DEA's popularity has grown rapidly since its inception, and it continues to be a valuable tool for decision-makers in various fields (Emrouznejad et al.; 2018). Standard DEA models evaluate the relative efficiency of DMUs based on their input and output data, but they do not provide information on estimating the amount of inputs and/or outputs needed to achieve efficiency targets. To determine these data, an inverse DEA model must be solved. This requires the development of appropriate mathematical models that are capable of solving the associate inverse problems. Wei et al. (2000) and Amin et al. (2017) highlighted the importance of solving inverse DEA problems and contributed to the development of related mathematical models. However, the challenge of solving inverse DEA problems is still an ongoing research area, and there is a need for