An Intuitionistic Fuzzy DEA Model with Undesirable Outputs Based on Multi-Objective Linear Programming

Data Envelopment Analysis (DEA) is a widely used mathematical programming technique for evaluating the efficiency of homogeneous decision-making units (DMUs). In many practical applications, however, data are imprecise and uncertain, especially in the presence of undesirable outputs such as pollution, waste, and emissions. Traditional DEA models are unable to adequately handle such uncertainty, which may reduce the reliability of efficiency assessments. To address this issue, intuitionistic fuzzy sets provide a more flexible framework by considering membership, non-membership, and hesitation degrees simultaneously. In this paper, a new intuitionistic fuzzy DEA model with undesirable outputs is proposed based on the framework of Multi-Objective Linear Programming (MOLP). The developed approach simultaneously optimizes multiple conflicting objectives, including input reduction, maximization of desirable outputs, and minimization of undesirable outputs. The MOLP structure enables a more comprehensive treatment of trade-offs among performance measures under uncertain environments. The proposed model generates a real-valued efficiency score for each DMU and provides a realistic framework for efficiency evaluation in complex systems with uncertainty and undesirable outputs. The integration of intuitionistic fuzzy theory and MOLP extends the applicability of DEA in environmental assessment, sustainable production, and other multi-criteria decision-making problems.

کلیدواژه ها:

Data Envelopment Analysis (DEA) ، Intuitionistic Fuzzy Sets ، Multi-Objective Linear Programming (MOLP) ، Undesirable Outputs ، Efficiency Evaluation ، Uncertainty Modeling