OICC Press Mathematical Sciences 2251-7456 19 1 (March 2025) 2025 03 31 Fixed Cost Allocation with a Minimum Distance to Fair Allocation in Fuzzy Data Envelopment Analysis 10.57647/mathsci.2025.1901.04 EN Elahe Sarfi Department of Mathematics, Damghan Branch, Islamic Azad University, Damghan, Iran Esmat Noroozi Department of Mathematics, East Tehran Branch, Islamic Azad University, Tehran, Iran Farhad Hosseinzadeh Lotfi Department of Mathematics, Science and Branch, Islamic Azad University, Tehran, Iran Mohammadreza Shahriari Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran Tofigh Allahviranloo Research Center of Performance and Productivity Analysis, Istinye University, Istanbul, Turkiye https://orcid.org/0000-0002-6673-3560 Sovan Samanta Research Center of Performance and Productivity Analysis, Istinye University, Istanbul, Turkiye; Department of Mathematics, Tamralipta Mahavidyalaya, WB-721636, India; Department of Technical Sciences, Algebra Bernays University, Gradiscanska 24, 10000 Zagreb, Croatia https://orcid.org/0000-0003-3200-8990 Leo Mrsic Department of Technical Sciences, Algebra Bernays University, Gradiscanska 24, 10000 Zagreb, Croatia; Rudolfovo Science and Technology Centre Novo Mesto, Slovenia Journal Article 2025 03 31 Resource allocation in Data Envelopment Analysis (DEA) has been extensively studied, yet most works focus on redistributing available resources rather than allocating unavoidable fixed costs among decision-making units (DMUs). This study addresses the important problem of fixed cost allocation, aiming to ensure that inefficient DMUs can become efficient while keeping allocations as fair as possible, thereby providing a practical decision-making tool in real-world contexts such as banking and manufacturing. We propose a new linear DEA-based model that allocates fixed costs so as to transform an inefficient DMU into an efficient one, with the objective of minimizing the deviation from fair allocation. The model is then generalized to a fuzzy environment by incorporating triangular fuzzy numbers for inputs, outputs, and costs, and validated using benchmark datasets from Cook and Kress and Wang et al. The results demonstrate that the proposed models can successfully enhance the efficiency of targeted DMUs while producing allocations close to fairness, and the fuzzy extension proves robust in handling imprecise data. The key novelties of this research are (i) introducing a linear efficiency-improving allocation model with minimum distance to fairness, (ii) extending the allocation problem to fuzzy DEA by considering fuzzy costs alongside fuzzy inputs and outputs, and (iii) showing that this integrated framework has not been addressed before in the literature, thereby offering a novel, practical, and equitable approach for fixed cost allocation in DEA.