Improving weak efficiency frontiers in the fuzzy data envelopment analysis models

Performance Evaluation of Airport Construction Energy-saving based on DEA Network Design and Performance Analysis

In some real-world applications, performance assessment often has to be considered under uncertainty with fuzzy input and output. Data envelopment analysis (DEA) is a new and effective approach to measuring the relative efficiency of decision-making units concerned with performance evaluation. Recently, DEA has studied with fuzzy input and output. In this paper, we present a computational procedure using a robust ranking method to formulate and solve fuzzy DEA models. Based on this approach, we convert the fuzzy DEA input and output from fuzzy form to crisp. Also, a case study has been presented as in [6] to assess the performance of eight manufacturing enterprises with fuzzy data.

In this study, banks’ business performance efficiency was analysed using data envelopment analysis (DEA), with expense categories as inputs and income categories as outputs. By incorporating a bootstrap method and a fuzzy data approach into a DEA model, additional insights and sensitivity analysis of the results were obtained. This study shows how fuzzy and bootstrap DEA can be used for investigating real market problems with uncertain data in an uncertain sample. The empirical analysis was based on the period of 2009–2018 for a sample of seven of Croatia’s largest private banks. The aim of the study was also to interpret the DEA results with regards to the specific market, legal, and macroeconomic conditions, caused by the changes introduced in the last decade. The results, and the changes in the inputs and outputs over time, revealed that the market processes occurring in the observed period had a significant impact on banks’ business performance, but led to a more efficient banking system. Two banks were found to be dominant over the others regardless of the changes in the sample and data fuzziness. DEA results were additionally compared to the most important financial indicators and accounting ratios, as an alternative or additional measure of banks’ efficiency and profitability.

Organizations are continuously endeavoring to provide a healthy work environment without any incident, by Health, Safety, and Environment (HSE) management. As most of the activities and processes in the organizations have risk-taking nature, identification and evaluation of risks can be useful to decrease their negative effects on the system. Although Failure Mode and Effect Analysis (FMEA) technique is used widely for risk assessment, the traditional Risk Priority Number (RPN) score has shortcomings like do not considering different weights and the inherent uncertainty of risk factors as well as do not regarding all viewpoints of the experts in decision making. The aim of this study is presenting a hybrid approach based on the Linguistic FMEA, Fuzzy Inference System (FIS) and Fuzzy Data Envelopment Analysis (DEA) model to calculate a novel score for covering some RPN shortcomings and the prioritization of HSE risks. First, after identifying potential risks and assigning values to the RPN determinant factors by linguistic FMEA team members due to the differentiation of these values, FIS is used to reach a consensus opinion about these factors. Then, the outputs of FIS are used by the fuzzy DEA and its supper efficiency model to risk prioritization which can contribute to full prioritization. In addition to considering uncertainty and decreasing dependence on the team’s opinions, in this phase weights of triple factors are calculated based on mathematical programming. To show the ability of the proposed approach in terms of HSE risks prioritization, it has been implemented in an active company in the chemical industry. After identifying risks having high priority based on the proposed score, preventive/corrective actions are presented in accordance with the case study, and for more analysis of results, the self-organizing map has been applied in this study.

Fuzzy data envelopment analysis based upon fuzzy arithmetic with an application to performance assessment of manufacturing enterprises

A new ranking method to fuzzy data envelopment analysis

Data envelopment analysis (DEA) is a linear programming-based methodology to measure the relative performance efficiencies of DMUs which produce multiple outputs by consuming multiple inputs. The input data and output data can be considered as a linguistic variables characterized by fuzzy numbers. So, in the present study, we extend DEA to fuzzy DEA (FDEA) in which the input and output data are taken as fuzzy numbers (FNs), in particular triangular FNs. In this paper, we develop two FDEA models to measure the left hand and right hand relative performance efficiencies of each DMU using $$\alpha$$ -cut approach. Further, we propose a ranking method to rank the DMUs based on left hand and right hand efficiencies. Finally, the developed FDEA models and proposed ranking models are illustrated with an example and then compared the results with geometric average efficiency index ranking method. The proposed model is also experimented with a real-life problem.

Measuring the eco-efficiency of municipal solid waste service: A fuzzy DEA model for handling missing data

ABSTRACTInformation systems (IS) are strongly influenced by changes in new technology and should react swiftly in response to external conditions. Resilience engineering is a new method that can enable these systems to absorb changes. In this study, a new framework is presented for performance evaluation of IS that includes DeLone and McLean’s factors of success in addition to resilience. Hence, this study is an attempt to evaluate the impact of resilience on IS by the proposed model in Iranian Gas Engineering and Development Company via the data obtained from questionnaires and Fuzzy Data Envelopment Analysis (FDEA) approach. First, FDEA model with α-cut = 0.05 was identified as the most suitable model to this application by performing all Banker, Charnes and Cooper and Charnes, Cooper and Rhodes models of and FDEA and selecting the appropriate model based on maximum mean efficiency. Then, the factors were ranked based on the results of sensitivity analysis, which showed resilience had a significantly higher impact on the proposed model relative to other factors. The results of this study were then verified by conducting the related ANOVA test. This is the first study that examines the impact of resilience on IS by statistical and mathematical approaches.

<abstract><p>Data Envelopment Analysis (DEA) is a prominent technique for evaluating the performance and ranking of a set of decision-making units (DMUs) that transform multiple inputs into multiple outputs. However, one of the challenges of the primary DEA models is facing imprecise data in real practical problems. To address this issue, fuzzy DEA have been proposed, which have been successfully applied in many real fields. On the other hand, in some real-world DEA applications, the primary objective of performance evaluation is the ranking of a group that consists of several DMUs that are typically under the control of a centralized management. In this paper, we try to use the theory of cooperative games and Shapley value method as a fair method to solve such games in order to rank groups in DEA. In this way, the resulting rank for groups is based on the average marginal shares of groups in different coalitions that are formed based on the theory of cooperative games. We applied the proposed method to rank groups of airlines considering fuzzy data. To the best of authors' knowledge, so far, no method has been presented in DEA literature for ranking groups in fuzzy environment and using game theory techniques.</p></abstract>

In this paper, we focus on measuring the performance efficiencies of decision making units (DMUs) using dual slack based measure (SBM) model with fuzzy data in data envelopment analysis (DEA). In the conventional dual SBM model, the data and the weights of input and output are found as crisp quantities. However, in real world applications, input-output data and input-output weights may have vague/uncertainty due to various factors such as quality of treatment and medicines, number of medical and non-medical staffs, number of patients, etc. in health sector. To deal with such uncertainty, we can apply fuzzy set theory. In this paper, we propose a SBM model with fuzzy weights in Fuzzy DEA (FDEA) for fuzzy input and fuzzy output. This model is then reduced to a crisp LPP by using expected values of a fuzzy number (FN). Finally, we present an application of the proposed model to the health sector, consisting of two input variables as (i) sum of number of doctors and staff nurses (ii) number of pharmacists and two output variables as (i) number of inpatients (ii) number of outpatients. Both the input variables and output variables are considered as TFNs.

In recent years, green movements, governments and institutions have forced many companies to improve their environmental performance. As a result of this growing interest in the environment, many companies have created integrated relationships with their suppliers to present green products and services. In public transportation sector, the evaluation and selection of appropriate green suppliers according to green supply chain management (GSCM) criteria is necessary for the sustainable development of urban transportation system. Among many evaluation selection methods, data envelopment analysis (DEA) is an appropriate method for green supplier selection. In this paper, to select green suppliers, we propose a new fuzzy double frontier DEA model in the presence of undesirable outputs and dual-role factors. A case study is presented to illustrate the applicability of the proposed model.

Data Envelopment Analysis (DEA) models and Free Disposal Hull (FDH) models were proposed almost simultaneously in the scientific literature. In DEA models, the constraints generate a convex set. For this reason, optimization methods and software are widely used for modeling and computations for these models. Non-convexity of production possibility set of FDH models refrained significantly the development of these models. In this paper, two methods are proposed for two-and three-dimensional frontier visualization for DEA and FDH models. Computational experiments using real-life datasets documented reliability and effectiveness of proposed methods.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Traditionally, the HDI is calculated under the assumption that all component indices are given the same weights. Although this assumption has been supported in the Human Development Reports, it has met also considerable criticism in the literature. In this paper, we present a new model to determine the weights of component indices in the light of data envelopment analysis (DEA). We develop a fuzzy multiple objective DEA model to assess the relative performance of the countries in terms of human development by using optimal weights for the component indices of the HDI.