Common-weights fuzzy DEA model in the presence of undesirable outputs with ideal and anti-ideal points: development and prospects

Abstract

In the literature, fuzzy arithmetic approach has been used for solving fuzzy data envelopment analysis (DEA) model in the presence of undesirable outputs by use of ideal and anti-ideal decision-making units (DMUs). In order to obtain the best and the worst fuzzy efficiencies, such an approach allows each DMU to choose the most favorable weights via optimizing its own evaluation that needs to solve various mathematical models. On the one hand, solving one mathematical model to find the best fuzzy efficiency and solving another mathematical model to find the worst fuzzy efficiency for each DMU increase the computational complexity significantly. On the other hand, the fully flexibility of weights leads to the different set of weights that may not be desirable. This paper proposes a common-weight method from two optimistic and pessimistic perspectives in fuzzy environment to determine the common sets of weights (CWS) to compute the best and the worst fuzzy efficiencies of all DMUs. The advantages of using common-weight DEA method based upon fuzzy arithmetic approach are reduction of the computational complexities and evaluation of equitably the best and the worst fuzzy efficiencies on the same base. The proposed approach first solves one linear programming model to compute each of the best and the worst fuzzy efficiencies of all DMUS, and then combines them to find a relative closeness index for the overall assessment. The developed approach is illustrated by a numerical example form the literature and the obtained results are compared with those from the existing ones.

Methods of regulatory impact assessment: critical analysis and alternative method

“…Also, the proposed method is neither simplistic nor complex and provides a sure solution without requiring tedious parametric tests. Likewise, it appropriately deals with the problem of uncertainty through the fuzzy method (Majdi et al , 2023).…”

“…That is why partial observability should be retained for analyzing this regulation. However, the assumption of fully observability, evoking the partial observability issues, is adopted because the Markov decision process does not fit sufficiently with the AVRM used in this work, which can take uncertainty into account via the fuzzy method (Majdi et al , 2023). In addition, the Markov decision process requires profound investigation leading to the choice of probabilistic distribution, which is suitable with the data of this work composed by outputs uniquely, the matrix of transition from a state before regulation to a state after regulation, the sequential and conditional actions presented as a decision tree and the reward function.…”

Using Big Data to enhance data envelopment analysis of retail store productivity

“…The DEA, which we employ in our study, has been extensively utilized in the retail sector (Vaz et al, 2010) and various other domains (e.g. Majdi et al, 2023). Many studies have endeavoured to enhance DEA by proposing innovative methodologies that often incorporate it with other techniques (Ebrahimnejad and Lotfi, 2012;Ebrahimnejad and Tavana, 2014;Nasseri et al, 2018), thereby increasing its versatility, accuracy, reliability and applicability.…”

“…The DEA, which we employ in our study, has been extensively utilized in the retail sector (Vaz et al ., 2010) and various other domains (e.g. Majdi et al. , 2023).…”