ANALYSIS OF SPECIFIC STATES IN NONPARAMETRIC DECISION-MAKING METHODS - Salehi, Kiyana; Mehrabian, Ahmad; Amoozad Khalili, Hossein; Navabakhsh, Mehrzad (International Journal of Industrial Engineering: Theory, Applications and Practice, 2022)
Decision-making techniques have now been developed more than ever before; however, it does not mean that the proposed models are impeccable and flawless. One of the most common multi-criteria decision-making methods is Data Envelopment Analysis (DEA), which is a nonparametric decision-making method as the weights of evaluation attributes are unknown in this model, and it is necessary to employ Linear Programming (LP) models to find them. This paper aims to analyze some specific states in existing problems in this area for the solution of which basic models might be slightly inefficient. Therefore, a few mathematical theorems are introduced and proven in this study to solve these problems. The proposed method avoids increasing the number of decision-making attributes and constraints in non-input DEA models. The results show that the proposed approach improved the simplex algorithm’s performance in solving the related linear programming models.