Fuzzy Data Envelopment Analysis with expected value approach and ranking using Genetic algorithm (original) (raw)
Related papers
Data Envelopment Analysis with Fuzzy Parameters
Optimizing, Innovating, and Capitalizing on Information Systems for Operations
Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. In the conventional DEA, all the data assume the form of specific numerical values. However, the observed values of the input and output data in real-life problems are sometimes imprecise or vague. Previous methods have not considered the preferences of the decision makers (DMs) in the evaluation process. This paper proposes an interactive evaluation process for measuring the relative efficiencies of a set of DMUs in fuzzy DEA with consideration of the DMs’ preferences. The authors construct a linear programming (LP) model with fuzzy parameters and calculate the fuzzy efficiency of the DMUs for different a levels. Then, the DM identifies his or her most preferred fuzzy goal for each DMU under consideration. A modified Yager index is used to develop a ranking order of the DMUs. This study allows the DMs...
Application of Fuzzy Data Envelopment Analysis in Decision Making
International Journal of Computer Applications
In this research work, Data Envelopment Analysis (DEA) is broadly connected in assessing the productivity of banks since it may be a strategy able of assessing the proficiency of choice making units in utilizing different inputs to deliver numerous yields. Be that as it may, a few yields of banks, in truth, have Fuzzy property, whereas ordinary DEA approach can as it were evaluate productivity with a fresh esteem and is incapable to assess loose information. Hypothetically, the Fuzzy Data Envelopment Analysis (FDEA) approach can assess banks' productivity more reasonable and exact since it can take the fuzzy property of inputs and/or yields into thought. The comes about appear that the FDEA approach could not as it were successfully differentiate instability, but too may have a better capability to segregate banks' effectiveness than the ordinary DEA method.
An Economic Mathematical Fuzzy Model for Data Envelopment Analysis
Journal of Namibian Studies : History Politics Culture
Performance assessment is a central to the management process in any type of organization. In addition, making rational economical decisions to improve organizational performance is a daunting task, as any organization is typically a multi-faceted entity which rely on complex systems that use uncertain information. Data envelopment analysis (DEA) is a powerful quantitative tool that makes use of multiple inputs and outputs to obtain useful information about the performance and efficiency of an organization. In many real-life applications, observations are usually fuzzy in nature. Therefore, DEA efficiency measurement may be sensitive to such variations. The purpose of this study is to develop a unified economical fuzzy DEA model that handles variables of different natures (vague and deterministic) independently and can be adapted to both input- and output-oriented problems, whether it is constant/variable return to scale. To handle fuzzy variables specially the economic variables in...
Fuzzy data envelopment analysis in the presence of undesirable outputs with ideal points
Complex & Intelligent Systems
Data envelopment analysis (DEA) is a prominent technique for evaluating relative efficiency of a set of entities called decision making units (DMUs) with homogeneous structures. In order to implement a comprehensive assessment, undesirable factors should be included in the efficiency analysis. The present study endeavors to propose a novel approach for solving DEA model in the presence of undesirable outputs in which all input/output data are represented by triangular fuzzy numbers. To this end, two virtual fuzzy DMUs called fuzzy ideal DMU (FIDMU) and fuzzy anti-ideal DMU (FADMU) are introduced into proposed fuzzy DEA framework. Then, a lexicographic approach is used to find the best and the worst fuzzy efficiencies of FIDMU and FADMU, respectively. Moreover, the resulting fuzzy efficiencies are used to measure the best and worst fuzzy relative efficiencies of DMUs to construct a fuzzy relative closeness index. To address the overall assessment, a new approach is proposed for ranki...
Ranking units in Data Envelopment Analysis with fuzzy data
Data Envelopment Analysis (DEA) is a widely applied approach for measuring the relative efficiencies of a set of Decision Making Units (DMUs), which use multiple inputs to produce multiple outputs. In real world problems the data available may be imprecise. With fuzzy inputs and fuzzy outputs, the optimality conditions for the crisp DEA Models need to be clarified and generalized. The corresponding fuzzy linear programming problem is usually solved using some ranking methods for fuzzy sets. The methods of solving fuzzy DEA problems can be categorized into four distinct approaches: tolerance approach, defuzzification approach, α-level based approach, and fuzzy ranking approach In this paper, we introduce a new α-level based approach and a numerical method for ranking DMUs with fuzzy data.
Fuzzy BCC Model for Data Envelopment Analysis
Fuzzy Optimization and Decision Making, 2003
Fuzzy Data Envelopment Analysis (FDEA) is a tool for comparing the performance of a set of activities or organizations under uncertainty environment. Imprecise data in FDEA models is represented by fuzzy sets and FDEA models take the form of fuzzy linear programming models. Previous research focused on solving the FDEA model of the CCR (named after Charnes, Cooper, and Rhodes) type (FCCR). In this paper, the FDEA model of the BCC (named after Banker, Charnes, and Cooper) type (FBCC) is studied. Possibility and Credibility approaches are provided and compared with an-level based approach for solving the FDEA models. Using the possibility approach, the relationship between the primal and dual models of FBCC models is revealed and fuzzy efficiency can be constructed. Using the credibility approach, an efficiency value for each DMU (Decision Making Unit) is obtained as a representative of its possible range. A numerical example is given to illustrate the proposed approaches and results are compared with those obtained with the-level based approach.
Expert Systems with Applications, 2009
Data envelopment analysis (DEA) requires input and output data to be precisely known. This is not always the case in real applications. This paper proposes two new fuzzy DEA models constructed from the perspective of fuzzy arithmetic to deal with fuzziness in input and output data in DEA. The new fuzzy DEA models are formulated as linear programming models and can be solved to determine fuzzy efficiencies of a group of decision-making units (DMUs). An analytical fuzzy ranking approach is developed to compare and rank the fuzzy efficiencies of the DMUs. The proposed fuzzy DEA models and ranking approach are applied to evaluate the performances of eight manufacturing enterprises in China.
Fuzzy integer-valued data envelopment analysis
RAIRO - Operations Research, 2018
In conventional data envelopment analysis (DEA) models, the efficiency of decision making units (DMUs) is evaluated while data are precise and continuous. Nevertheless, there are occasions in the real world that the performance of DMUs must be calculated in the presence of vague and integer-valued measures. Therefore, the current paper proposes fuzzy integer-valued data envelopment analysis (FIDEA) models to determine the efficiency of DMUs when fuzzy and integer-valued inputs and/or outputs might exist. To illustrate, fuzzy number ranking and graded mean integration representation methods are used to solve some integer-valued data envelopment analysis models in the presence of fuzzy inputs and outputs. Two examples are utilized to illustrate and clarify the proposed approaches. In the provided examples, two cases are discussed. In the first case, all data are as fuzzy and integer-valued measures while in the second case a subset of data is fuzzy and integer-valued. The results of t...