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Assessment of energy and environmental issues has received a special focus due to global warming and climate vulnerabilities. Energy, environment and economic development simultaneously yielded a combined effect, which is favorable from the economic point of view while unfavorable for environmental aspects. It is necessary to measure the energy, economic and environmental performance with appropriate methods. Therefore, this study measures the energy security and environmental efficiency. Data used in this study were collected from the official website of International Energy Agency (IEA), British Petroleum (BP) and World Bank. We employ a non-radial DEA technology for assessing energy and environmental efficiency for OECD countries. Results reveal that the values of energy intensity range from 0.93 to 0.30, while GHG emission per capita values range from 24.61 to 5.28. As far as energy efficiency is concerned, Iceland and Greece have the highest value (0.99) of energy efficiency, whereas Ireland has the lowest value (0.34) of energy efficiency. Further analysis shows that Austria, Australia, Portugal and Spain have values between 0.70 and 0.80 for energy intensity, whereas UK, Sweden, Germany and the USA have values of energy intensity, between 0.40 and 0.50. The study proposes some valuable policy implications for policy and decision-makers to make the environment more sustainable.

An efficiency measure, referred to as "Range-Adjusted Measure (RAM)," has been recently proposed as a new type of DEA (Data Envelopment Analysis) performance evaluator. To deal with large RAM problems, this study documents how the algorithm of RAM is designed in a parallel process. The parallel process is implemented on a Local Area Network (LAN) that connects multiple personal computers. A large simulation study examines the performance of the proposed RAM network computing approach.

The aim of this paper is to explore the efficiency and the benchmarks of financial holding companies (FHCs) for a small open economy, Taiwan. We employ a two-stage production process including profitability and marketability performance using a non-parametric frontier method — data envelopment analysis (DEA). Furthermore, the factor-specific measure and BCC (Banker–Charnes–Cooper) model are combined together not only to identify the inputs/outputs that are most important but also to distinguish those FHCs which can be treated as benchmarks. Our empirical result shows that (1) big-sized FHCs are generally more efficient than small-sized ones; (2) FHCs with the main body of insurance averagely perform better than the other two types (banks and securities); (3) while small efficient FHCs are easily to become benchmarks, big efficient FHCs are deemed as competitive niche players; (4) further mergers and acquisitions among FHCs should be considered so as to achieve economies of scale. The profitability/marketability matrix of FHCs is also presented.

This paper suggests a new DEA-based approach to the identification of the efficient category value for controllable categorical factors when controllable categorical factors are involved in the DMU's production process. Though the main objective of this approach is to identify the efficient category value for nonhierarchical categorical factors, it can also be employed to identify the efficient category value for hierarchical categorical factors without any large adjustment. This new approach is applied to an empirical data from an Agricultural Resource Management Survey for soybean production in Pennsylvania. The result of the empirical application illustrates how our approach leads us to the efficient category value of categorical factors by using both radial measure and nonradial measure. Moreover, it shows us the existence of substantial heterogeneity in the use of categorical factors and the opportunity of improvement in DMUs' selection of category value for categorical factors.

The “Dynamic-network” version of cost efficiency measurement in Data Envelopment Analysis (DEA) is proposed in this paper. The classical DEA models ignore operations of individual processes within a system; moreover, they compute efficiency at the same time. Therefore, we suggest a relational model to estimate cost efficiency in static network structures. Also, we incorporate the dynamic effect in network structures. The proposed models here evaluate the overall efficiency over the whole periods and indicate it as a weighted average of period efficiencies. The main advantage revealed in this study is recognition of: which divisions at what periods caused the inefficiency of the system, the internal activities of the system over time, considered; moreover, the results obtained here is applicable in, improving the performance of the system. A case study of Iranian banking industry is used to show the applicability of the approach.

An important area of research involving the benchmarking methodology data envelopment analysis (DEA), concerns the modeling of multistage situations. In the usual multistage settings, it is generally assumed that all decision-making units (DMUs) have the same number and configuration of stages. However, in many real-world examples, this assumption does not hold. Consider, for example, a supply chain setting where for some DMUs, products are shipped directly from a supplier to a retailer (single-stage), while for other DMUs, products can be transshipped through distribution centers (two or more stages). In the current paper, we investigate an efficiency measurement situation where the DMUs exhibit a mix of single and two-stage setups. The particular case examined involves a set of high technology firms that can be thought of as falling into two groups; those firms where the output of interest is the annual revenue generated, and those that not only generate revenue, but as well invest a portion of that revenue in R&D. Firms in the first group can be viewed as being single-stage DMUs while those in the other group are of the two-stage type. The modeling complication here is that the set of DMUs do not explicitly form a homogeneous set of units. We develop a DEA-style model aimed at measuring efficiency in the presence of such nonhomogeneous two-group structures.

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of a set of decision-making units (DMUs), based on their multiple inputs and outputs. The original model is based on the assumption that DMUs operate independently of one another. However, this assumption may not apply in some situations, as in the case we present in this paper, in which DMUs can work together to produce joint outputs. What makes it more interesting is the situation in which this characteristic of sharing outputs among some DMUs differs from one DMU to another; this makes it more challenging to determine independent efficiency scores that cater for this phenomenon. To address this, the current paper presents a methodology for measuring efficiency in situations in which DMUs share outputs with other units. We examine the case of a set of research groups in a Mexican university. For this study, the inputs used are professors belonging to various groups, and outputs are the published journal articles, some of which are produced completely within a group, whereas others arise from collaboration with professors from other research groups. Jointly published articles form a link connecting the groups.

Conventional Data Envelopment Analysis (DEA) models assume real-valued input-output data and ignore the collaboration among decision-making units (DMUs) in the analysis of efficiency. This paper proposes a novel DEA production technology that is capable of dealing with the collaboration concept and resource sharing for both integer and real-valued data in the efficiency measurement and target setting. This is accomplished by way of a mixed integer linear programming (MILP) efficiency analysis model. We also deal with the computational aspect of the proposed model and invent a MILP with the computational complexity of $O(n+m-p)$ rather than $O(2^n+m-p)$. We explain the proposed models by numerical examples and graphical illustrations. We apply our models for efficiency analysis of 15 governmental (public) hospitals in Mashhad City in Iran and demonstrate the practical relevance and advanced future of the proposed methods.

This paper presents a method for ranking a set of decision making units according to their level of efficiency and which takes into account uncertainty in the data. Efficiency is analysed using fuzzy DEA techniques and the ranking is based on the statistical analysis of cases that include representative situations. The method enables the removal of the sometimes unrealistic hypothesis of a perfect trade-off between increased inputs and outputs. This model is compared with other DEA models that work with imprecise or fuzzy data. As an illustration, we apply our ranking method to the evaluation of a group of Spanish seaports, as well as teams playing in the Spanish football league. We compare the results with other methods and we show that our method enables a total ranking of the seaports, and that the ranking of football teams is found to be more consistent with final league positions.

The objective of this study is to analyze and assess multi criteria quality of products by an integrated multivariate approach. The integrated multivariate method is based on data envelopment analysis (DEA), principle component analysis (PCA) and numerical taxonomy (NT). To achieve the objective of this study 51-product quality indicators were identified. These indicators (inputs and outputs) were classified as direct and indirect product quality indices and they were classified according to balanced score card (BSC) arrangements. To show the applicability of the proposed approach, five random indicators were considered for seventeen workshops within a large machinery manufacturer. Moreover, PCA, DEA and NT were applied to the set of data. Furthermore, PCA and NT are used to verify and validate the findings of DEA. The results (ranking) of the three approaches were then compared by non-parametric Spearman and Kendall Tau correlation techniques. The results of the non-parametric analysis show should high level of correlation between the three approaches. Previous studies evaluate quality characteristics based on a set of selected criteria that does not reflect total quality characteristics, whereas this study proposes a total multi criteria quality approach to overcome these shortcomings. Moreover, this is the first study to utilize and apply an integrated multivariate approach based on DEA, PCA and Numerical Taxonomy for assessment, ranking and verification and validation of industrial units based on multi criteria quality characteristics. This means that DEA is used for ranking, PCA is used for evaluation of the importance of each indicator and NT is used for validation and verification purpose. The approach of this study may be applied to other manufacturers for total quality assessment of quality characteristics.

This paper is focused on the multiobjective design of equivalent accelerated life test (ALT) plans. Equivalent ALT plans are expected to achieve the same statistical performance as a baseline ALT plan yet lead to other desired performance measures such as reduced test time and total cost. Before determining the desired multiobjective equivalent ALT plans, an efficient fast non-dominated sorting genetic algorithm (NSGA-II) is utilized to identify a set of Pareto optimal solutions. To handle a large number of Pareto optimal solutions, a self-organizing map (SOM) and data envelopment analysis (DEA) are sequentially applied to classify the Pareto solutions and reduce the size of the suggested solution set. This integrated approach allows for the tradeoff of information among the Pareto solutions and the reduction in the size of the solution set. It provides a useful tool for practitioners to make meaningful decisions in planning ALT experiments.

The DEA model is applied to analyze the operational efficiency of Taiwan's publicly-listed banks to reveal the influence of the rising non-performing loan ratio (NPLR) on Taiwan's banking industry. After taking into account the NPLR and the different classifications of banks, the respective performances of different types of banks exhibit the following variations and characteristics: the originally more-efficient new private banks significantly fell behind the old public banks in terms of efficiency scores after the NPLR was included. The old private banks' operational efficiency fell behind that of the other banks at all times. The productivity of all new private banks exceeded that of the other banks.

This study fills a huge gap in literature by providing some evidence on the level and determinants of bank efficiency in a Pacific island context. DEA results show that overall efficiency levels may be lower than in Australia, the home country of major banks. Dynamic GMM and panel data results show that personnel expenses and bank credit matter for efficiency, but not other bank-specific and macroeconomic factors. These insights substantially improve policy-making capacities for Fiji and other Pacific economies, including Papua New Guinea, Samoa, Tonga, Solomon Islands and Vanuatu where banking and regulatory systems and structures are highly comparable.

Data Envelop Analysis (DEA) and Analytic Hierarchy Process (AHP) are widely used methods in ranking decision alternatives. However, current DEA models are difficult to discriminate decision-making units through articulating the decision makers' preferences. While AHP and Gower plot models have to specify complete pairwise preferences without providing assisting information. This study develops an iterative method of ranking decision alternatives by integrating DEA, AHP and Gower plot techniques. The developed method first utilizes a modified DEA model to narrow the ranges of a decision maker's preferences. Then, the tentative ranks of the decision alternatives, computed by embedding the decision maker's preferences, are depicted via Gower plots to illustrate the cardinal and ordinal inconsistencies of these preferences. The decision maker then adjusts the preferences iteratively until the inconsistencies are within the tolerance.

This paper focuses on the problem of resource allocation through data envelopment analysis. We propose three integrated models for allocating resources. The first model aims at minimizing the input consumption, the second one aims at maximizing the total outputs within the current resources, and the last one aims at maximizing the total outputs using the predicted resources in the next production season. Since the number of inputs or outputs is usually more than one, the abovementioned issue is often a multiple objective linear programming (MOLP) problem. Through the proportion of inputs (outputs) of new decision making unit (DMU) to the total inputs (outputs) of all old DMUs, we transform the MOLP problem into a single objective linear programming model. We assume that decision maker must ensure that the expected outputs of each DMU after allocation in the next production season are not less than this production season. All these proposed models have the same advantage that the results gained from the models are Pareto efficient. A numerical example of 25 supermarkets is used to illustrate our proposed approach.

In many countries, including Iran, Provincial Departments of Physical Education try to develop the athletic sports and sports for all in their related areas (state), using the government resources. Their success rate has always been an important subject for the top sports managers of country. In this paper we use data envelopment analysis (DEA) and analytic hierarchy process (AHP) techniques for analyzing the performance of physical education organizations in Iran. Some convex and nonconvex DEA models have been used. Afterwards, we have used the Shannon's entropy for aggregating the results obtained from different models and providing a final efficiency score (FES) and a unified ranking. It can be seen that, in the ranking approach provided in this paper the most productive scale size (MPSS) units have the best rank (see Proposition 1). Our findings reveal that the average of FESs of the states is 0.472635 and 50% of the states have an FES more than this average. Classifying the sates to five efficiency classes, "Excellent, Good, Middle, Weak and Very Weak", the percentage of the states belonging to these classes are 6.7, 30, 16.6, 36.7 and 10, respectively. Also, some correlation and difference studies have been carried out using the Pearson's correlation and student's t-tests. Finally, comparisons between the results of some relevant existing publications and those given in the present paper are addressed.

This paper deals with inverse DEA from both theoretical and applied viewpoints. First, some methodological approaches and theoretical results are outlined; and then an application of DEA with real-world data (for assessing educational departments in a university) is addressed. Afterwards, possible extensions and applications of the existing approaches in the presence of fuzzy data are developed. The final theoretical part of the paper contains a main theorem which provides a sufficient condition for efficiency maintaining in the presence of fuzzy data. To do this, we have used some notions/results from multi-objective decision-making theory.

This study used dynamic data envelopment analysis (dynamic DEA) to evaluate the operating performance of life insurance companies in Taiwan and China. In addition, this study adopted panel data regression, which employs the cross-section and time-series approaches, to investigate the impact of intellectual capital (IC) on operating performance. The results indicated that the overall performance of life insurance companies in China was better than that of life insurance companies in Taiwan. Furthermore, in both countries, the performance of life insurance companies with local capital was better than that of companies with foreign capital. The results also showed that human capital (HC) and structural capital (SC) had impacts on the operating performance of life insurance companies. The potential applications and strengths of DEA in assessing the life insurance industries in Taiwan and China are highlighted.

Increased global competition has led to a slowdown in Taiwan’s domestic semiconductor industry growth, which has resulted in many semiconductor companies reducing their investments and or seeking mergers and acquisitions (M & As) to increase market power, expand their business territories or increase their competitive edge. However, as there is general uncertainty regarding the efficiencies to be gained from these M & As, there has been an increase in M & A supervision. While past research has explored company operations and management efficiency after mergers, there has been less focus on potential mergers. Therefore, this study used a resample slacks-based measure (RSBM) and merger potential gains models to evaluate potential merger efficiency gains. Data on 29 Taiwanese-listed integrated circuit (IC) design industry firms were collected to evaluate the efficiency of potential M & As, from which it was found that the potential M & As efficiencies had positive and negative values, indicating that efficiency gains were not guaranteed. A positive value was found for a potential M & A between MTK & NOVATEK and MTK & DAVICOM, which meant that a potential M & A would increase operating efficiencies and reduce costs.

The objective of this paper is to clarify whether or not the so-called innovation leaders are efficient in transforming innovation inputs into outputs. The study aims to expand on the thought that the level of inputs is decisive in classification of countries as leaders, followers, or laggards in the race to improve innovativeness and competitiveness, and thus raise the standard of living. Based upon the European Innovation Scoreboard (EIS), the efficiency of investment in innovation is examined with the use of the DEA model. The use of the EIS as the main source imposes a limitation on the scope of the countries examined, yet the EIS is essentially the only comprehensive source that examines innovativeness. It is observed that the so-called laggards in innovation are often efficient in their use of resources, whereas leaders of innovation fall short in the area of returns to scale and congestion. Such an observation provides an important guide to the development of policies aimed at improving innovative efforts. Finally, through the use of the nonparametric DEA model, this paper provides a methodological extension to the methods for investigation of innovation systems.