A new ranking method to fuzzy data envelopment analysis

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

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.

<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>

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

Globally, insurance and takaful companies play a crucial role in economic and financial development of a country. Malaysia’s insurance sector has undergone substantial changes compared to two decades ago. Due to intense competition observed in Malaysian insurance industry, efficiency measurement and a complete efficiency ranking of insurance companies are very important for the decision makers so that necessary changes and improvement can be made. Data Envelopment Analysis (DEA) is a non-parametric method that has been acknowledged as an effective method to measure the efficiency of homogeneous decision-making units (DMUs). The main advantage of DEA is its ability to handle multiple inputs and outputs. However, standard DEA has poor discrimination power since it generates too many efficient units especially when the number of DMUs under study is insufficient in comparison to number of inputs and outputs. It cannot discriminate efficient units and therefore unable to give a complete ranking of DMUs. This study aimed to overcome the ranking problem found in standard DEA by integrating DEA and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II. The hybrid method was applied to evaluate the efficiency and ranking of 22 life insurance and takaful companies in Malaysia from the period of 2017 to 2018. Input variables used in this study comprised of fees and commission, and management expenses. Meanwhile, output variables were net premium and generated investment income. The proposed method involved two stages. In the first stage, DEA was applied to obtain the efficient scores for the DMUs. In the second stage, PROMETHEE II was implemented to rank the efficient units. This hybrid method has successfully obtained full ranking of all the insurance companies under study. The inefficient companies can learn strategies and practices from efficient companies to enhance their services. It is recommended for future research to integrate DEA with other Multi Criteria Decision Making (MCDM) methods such as TOPSIS and VIKOR to fully rank the DMUs.

Survey on rank preservation and rank reversal in data envelopment analysis

Improving weak efficiency frontiers in the fuzzy data envelopment analysis models

Combining ranking scales and selecting variables in the DEA context: The case of industrial branches

Information technology (IT) is defined as the obtainment, procedure, storage and propagation of sounding, drawing, and textual information by combining microelectronics-based computing and telecommunications. Nowadays, IT is starting to spread further from the conventional personal computer and network technologies to integrations of other fields of technology such as the use of cell phones, televisions, automobiles, etc. In other words, IT has penetrated in daily life of human beings and become one part of the whole society. The importance of IT has become momentous. Therefore, to understand the performance of efficiency and productivity of the IT firms is critical for managers as well as for personal investors. Until now, there are very few researches tried to analyze final performance of the IT firms. As a result, this research intends to use traditional Data Envelopment Analysis (DEA) CCR or BCC models to evaluate the performance of IT firms. The Decision Making Units (DMUs) on this research are chosen from IT firms in S&P 500. However, the traditional DEA models are not fair models from the aspect of improper weight derivations. Thus, this paper intends to analyze the efficiency of IT firms in S&P 500 efficiencies by using multiple objective programming (MOP) based Data Envelopment Analysis (DEA). In a MOP based DEA approach, DMUs will be evaluated based on an equal standard and the results will be evaluated more fairly. The world’s leading IT firms in S&P 500 will be evaluated based on publicly available financial reports of the fiscal year 2010. In addition, the newly developed MOP can improve the traditional DEA’s unfair weights problems and benchmark the efficiency of IT firms in S&P 500 correctly. In the empirical study, the MOP based DEA demonstrated that F5 Networks should be the communications equipment companies of IT worthwhile to be invested. In the future, performance evaluation results can be served as foundations for investment strategies definition.KeywordsInformation Technology (IT)Standard and Poor’s 500 indexPerformance EvaluationData Envelopment Analysis (DEA)Multiple Objective Programming (MOP)

The theoretical concept of data envelopment analysis (DEA) was published by Charnes, Cooper and Rhodes in 1978. It is a powerful tool for assessing the efficiency of decision making units (DMUs) by comparing their respective inputfactors and output quantities. More precisely, the efficiency of a DMU is measured as the relation between the sum of weighted outputs and of weighted inputs, where the weights for a DMU are the solution of a linear optimization problem. But this approach suffers from a lack of objectivity due to the fact that every DMU wants an advantageous evaluation for itself, called self-appraisal in the respective literature. The purpose of this paper is to present an extension of the evaluation process in such a way that every DMU’s performance will be calculated in an objective and fair manner. For this a fuzzy oriented approach will be formulated which yields a nonlinear optimization problem. The resulting input/output weights are a compromise of all DMU’s individual interests. The so far developed theoretical concept will be applied to a company of Taiwanese semiconductor industry. Five DMUs’ efficiencies will be calculated over three years. Professional software solves the nonlinear program and the results permit meaningful economical interpretations.

This paper provides a comprehensive overview of Mass Rapid Transit (MRT) efficiency in selected ASEAN countries that includes Malaysia, Philippines, Thailand, Singapore, and Indonesia. MRT is one of the popular railways public transport that has become people’s alternative to move freely. However, inadequate study of MRT produces the question of whether this development is efficient or inefficient. Using Data Envelopment Analysis (DEA) to measure efficiency, this method can also identify the ways to make improvements to increase the efficiency of the MRT of ASEAN countries that are inefficient. Data obtained from the Ministry of Transport of each respective country were analyzed using Data Envelopment Analysis Online Software (DEAOS). The study identifies the efficiency score for CCR and BCC models for both input- and output-oriented. The analysis showed different results for CCR and BCC models. CCR model indicated that only Philippines and Singapore are efficient while BCC model indicated that Indonesia, Philippines, and Singapore are efficient. The improvement made for inefficient decision-making units (DMUs) only focus on BCC output-oriented model as it is more reliable to the real world since the reality have several constraints. The study reveals that inefficient DMUs which are Malaysia and Thailand must increase the number of passengers by 199.57% and 80.69% respectively to increase efficiency. The findings of this study benefit the decision-makers in pursuance of measuring the smoothness operations of MRT using inputs and output obtained.

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiency of decision making units (DMUs) which ‎consume the same types of inputs and producing the same types of outputs. Believing that future planning and predicting the ‎efficiency are very important for DMUs, this paper first presents a new dynamic random fuzzy DEA model (DRF-DEA) with ‎common weights (using multi objective DEA approach) to predict the efficiency of DMUs under mean chance constraints and ‎expected values of the objective functions. In the initial proposed†â€DRF-DEA model, the inputs and outputs are assumed to be ‎characterized by random triangular fuzzy variables with normal distribution, in which data are changing sequentially. Under this ‎assumption, the solution process is very complex. So we then convert the initial proposed DRF-DEA model to its equivalent multi-‎objective stochastic programming, in which the constraints contain the standard normal distribution functions, and the objective ‎functions are the expected values of functions of normal random variables. In order to improve in computational time, we then ‎convert the equivalent multi-objective stochastic model to one objective stochastic model with using fuzzy multiple objectives ‎programming approach. To solve it, we design a new hybrid algorithm by integrating Monte Carlo (MC) simulation and Genetic ‎Algorithm (GA). Since no benchmark is available in the literature, one practical example will be presented. The computational results ‎show that our hybrid algorithm outperforms the hybrid GA algorithm which was proposed by Qin and Liu (2010) in terms of ‎runtime and solution quality. ‎

The fuzzy data envelopment analysis (FDEA) method lacks sufficient discrimination power to rank efficient decision making units (DMUs) with fuzzy data; moreover, it evaluates DMUs using only self-evaluation. This paper develops the fuzzy cross efficiency (FCE) DEA model that combines self-evaluation with peer-evaluation to eliminate the weaknesses of traditional FDEA. This method solves the efficiency evaluation problem in fuzzy environments from a new perspective. An effective method is provided to solve this model, and the ranking results of DMUs at different -levels are obtained. Finally, an example is given to illustrate the proposed method in greater detail.

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

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.