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The Malmquist index constructed from distance functions is a useful tool for measuring productivity growth. In this paper, we assume the presence of random noise in empirical data and enhance the literature by introducing a panel-data model in stochastic nonparametric envelopment of data (StoNED) for the consistent estimation of the Malmquist-type index. The method we propose initially employs directional distance functions to evaluate both within and intertemporal efficiencies of Malmquist-type indices based on StoNED. A major feature of our approach is that it measures productivity changes over time while capturing both inefficiency and noise in a nonparametric multiple-input multiple-output setting. As an illustrative application, we investigate the productive performance of Japanese regional banks using a balanced panel dataset spanning 2008 to 2017. • We propose a stochastic nonparametric estimation of the Malmquist-type indices. • We establish a consistent estimator for measuring intertemporal efficiencies. • The proposed approach measures productivity changes under noise. • The results reveal the drivers of productivity growth of Japanese regional banks. [ABSTRACT FROM AUTHOR]

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis. Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking. [ABSTRACT FROM AUTHOR]

The performance evaluation method based on data envelopment analysis (DEA) is one of the important tools to measure the competitiveness and productivity of enterprises. However, the input and output of enterprises may contain negative data and the essence of DEA is an iterative optimization model, resulting in a low applicability of the DEA-based performance evaluation method in the real word, especially for the dilemma of evaluating enterprise performance within a limited time for new enterprises. Therefore, this study firstly develops a DEA model that can handle negative data for enterprise performance evaluation, and then further establishes a new method base on the extended belief rule-base (EBRB) model for enterprise performance online evaluation. A case study about 35 Chinese state-owned enterprises are conducted to verify the effectiveness of the proposed enterprise performance online evaluation method. Experimental results showed that the proposed method has capable of evaluating enterprise performance with accurate efficiency values better than some existing performance evaluation methods, and its computation time is significantly less than the DEA-based performance evaluation method, which guarantee that the proposed enterprise performance online evaluation method can serve as a reference for the promotion of enterprise productivity and sustainable economic development. [ABSTRACT FROM AUTHOR]

Nowadays, health systems comprise a series of resources structured to provide healthcare services to meet our health needs. However, premature deaths still occur. To quantify and understand personal healthcare conditions affecting such amenable mortality, the Healthcare Access and Quality Index (HAQI) was put forward, evaluating 195 countries and territories since 1990. Nevertheless, the literature acknowledges a series of limitations of this framework, such as the drawbacks of using principal component analysis to aggregate individual indicators, the absence of control for financing and environmental conditions, and the presence of a substantial degree of data uncertainty. Accordingly, we propose a methodological alternative to the computation of the HAQI using a novel fuzzy Data Envelopment Analysis model to handle the aforementioned shortcomings. We also propose its extension towards the quantification of efficiency (E-HAQI) – in the sense of value for money – by incorporating financial aspects as modelling inputs. This way, we contribute with innovative modelling approaches that can also deal with the high degree of data uncertainty. Furthermore, in a second-stage analysis, the impact of key exogenous factors on healthcare access and quality is assessed via non-parametric hypothesis testing. Our results show positive and significant correlations of both the revisited HAQI and E-HAQI with the original HAQI 2016 dataset. They also reveal a better use of resources by European and Oceanian countries and territories than by Sub-Saharan African ones. Concerning contextual determinants, socio-demographic development, human development, and the type of health system were found to be statistically significant drivers of healthcare access and quality efficiency. • Revisitation of the 2016 'Healthcare Access and Quality Index'. • Novel fuzzy Data Envelopment Analysis approach for performance assessment. • Modelling of uncertainty and undesirable factors. • Incorporation of financial aspects for efficiency measurement. • Non-communicable diseases show the greatest potential for improvement. [ABSTRACT FROM AUTHOR]

The efficiency of decision-making units with multiple inputs and outputs can be evaluated using a non-parametric quantitative method called data envelopment analysis (DEA). DEA is a decision-making technique for determining the efficiency of similar units. As a result of its simplicity and applicability, DEA has attracted the attention of scientists and researchers working in various fields, which has contributed to a rich literature in terms of theoretical development and examples. The early models of DEA with crisp inputs and outputs were based on linear programming. As opposed to fuzzy theory and stochastic programming, uncertain belief theory can be applied in the absence of historical data for inputs and outputs. Therefore, this paper firstly studies the slacks-based measure model of DEA in an uncertain environment, where inputs and outputs are uncertain based on belief degrees. The problem is modeled mathematically, and some properties of the model are demonstrated. The slacks-based measure model with belief-degree input and output uncertainties is also examined for stability and sensitivity. The final step of the study involves reporting our experience with the new uncertain slack-based measure model and the stability radius for each decision-making unit. [ABSTRACT FROM AUTHOR]

The WHO has declared that the COVID-19 pandemic is a severe health crisis. Currently, variants of concern are delta and omicron, including sub-lineages of the omicron which are XBB and BQ.1 variants. Decision planning with situation awareness is important during the COVID-19 pandemic, especially demand planning for medical supplies following pandemic probability or severity via pandemic risk assessment. Therefore, this research proposes an intelligent risk assessment on the prediction of the COVID-19 pandemic using deep learning with deep neural network (DNN) and tunicate swarm algorithm (TSA). The results show the model can accurately predict the distance and elapsed time of the next COVID-19 case based upon the previous case and evaluate the associated risks. The contribution of this research, as prediction model is based upon a DNN, it has the ability to learn and by implementing the TSA, it can improve theoretically the performance of the DNN for more precise prediction and faster convergence to the optimal solution. The prediction results are practically expanded to analyze risk assessment using probability and the data envelopment analysis (DEA). The benefit of this research is that the proposed methodology demonstrates the prediction results using risks assessment based upon intelligent risk assessment charts. The Government or those involved can use the proposed methodology to achieve a better decision-making and management to control the COVID-19 pandemic in terms of supplying the medical supplies into pandemic areas. [ABSTRACT FROM AUTHOR]

Exogenous constraints such as fixed-sum output can affect the efficiency assessment of traditional two-stage data envelopment analysis (DEA) models. To address this issue, several studies have evaluated the DEA efficiencies for two-stage systems with fixed-sum output. They have proposed centralized models based on cooperative game theory and models under global mindset. However, in many cases, the first and second stages play a noncooperative game, and information on stage priority is often unobservable. In this study, we develop a noncooperative two-stage fixed-sum output DEA model with uncertain stage priority for calculating the overall system and stage efficiencies. Instead of stage priority as an endogenous variable, with aid of acceptaiblty analysis, we analyze the dominance relationship among decision-making units (DMUs) and provide a robust ranking by considering all possible stage priorities. We also conduct an empirical analysis with data from commercial banks to demonstrate the effectiveness of the developed model. After 10,000 groups of stage priority iterations, we report a robust ranking of commercial banks in which Hefei branch ranks first with 100 % probability. [ABSTRACT FROM AUTHOR]

• Theoretical mechanism of the non-uniqueness optimal solution for cross-efficiency is revealed. • Weight priority is constructed to reflect the comparative advantage of inputs and outputs. • Weight priority strategy can serve as both an independent strategy and a supplementary strategy. • The uniqueness of optimal result obtained by weight priority strategy is theoretically proved. Due to a large number of studies on cross-evaluation strategies, the theoretical problem of DEA cross-efficiency approach has been transformed from the non-uniqueness of the optimal solution to the relative uniqueness, because the existing cross-evaluation strategies can only reduce the possibility of non-unique solutions, but cannot prove the existence of a unique optimal solution. This paper illustrates the relative uniqueness of optimal solution of the cross-efficiency approach with traditional cross-evaluation strategies, and the graphic method and theoretical analysis are used to explain the reasons of relative uniqueness. Based on the data characteristic of inputs and outputs, the weight priority rule is made for constructing the new cross-evaluation strategy, and its optimal solution is proved to be unique. In addition, the weight priority strategy can also be introduced into traditional strategies as a supplementary strategy, and then the unique optimal solution can be obtained on the premise of preserving the characteristics of traditional strategies. In general, this paper solves the non-uniqueness and relative uniqueness problem of optimal solution in the DEA cross-efficiency approach, and then the reliability and robustness of the approach are thus improved. [ABSTRACT FROM AUTHOR]

Industrial clusters play a pivotal role in securing competitive advantages and achieving national strategic objectives within the new energy sector. The assessment of new energy industrial clusters is crucial for sustaining their swift development. However, prior research has rarely addressed this domain. In this study, a Multi-Criterion Decision-Making (MCDM) approach is introduced for the assessment of new energy industrial clusters. This approach employs the Data Envelopment Analysis (DEA) super efficiency method alongside the Grounding-Enterprise-Market-Surrounding (GEMS) model to evaluate the efficiency and competitiveness of new energy industrial clusters, respectively. Subsequently, this approach categorizes new energy industrial clusters based on their efficiency and competitiveness scores. Clusters exhibiting similar scores in these areas are grouped together for the identification of key new energy industrial clusters. Ultimately, the selection of key new energy industrial clusters is performed based on varying criteria. A case study on the selection of prominent solar energy industrial clusters is presented to demonstrate the efficacy of the proposed approach. The findings indicate that this approach facilitates an effective selection process for key new energy industrial clusters. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a technique that allows each decision-making unit (DMU) to calculate efficiency using the most favorable weights for inputs and outputs. The resulting efficiency scores are incomparable and difficult to discriminate. Cross-efficiency is a concept for solving the problem of incomparability among the efficiencies of a set of DMUs that are calculated from different weights and is helpful for ranking. In basic DEA, several cross-efficiency evaluation methods with various secondary purposes have been presented, however, the internal mechanism of the DMUs in cross-efficiency evaluation is often neglected. In this study, we use the composition approach for a basic two-stage structure, so that the efficiencies of the divisions are estimated first, and then the overall efficiency of the system is obtained. To solve the linear bi-objective model in the combination approach, we consider a compromise programming problem to find the solution that is as close as possible to the ideal point. Finally, a secondary objective model is proposed to select a weight among the optimal weights of the compromise model, which also guarantees the efficiency of the optimal weight for the bi-objective problem. We use the optimal solution obtained from this model to evaluate cross-efficiency and rank DMUs for a series two-stage system. The proposed approach enables us to rank the DMUs by assessing the performance of each DMU and each of its separate divisions. The validity of the suggested network cross-efficiency evaluation is demonstrated by a numerical example from the literature. [ABSTRACT FROM AUTHOR]

Efficient resource allocation in healthcare organizations remains a complex challenge, exacerbated by the intricate data structure and myriad variables involved. While traditional methods like Data Envelopment Analysis have been employed, these often lack the requisite discriminatory power for accurate evaluation. This study introduces the PDA methodology, a fusion of Principal Component Analysis, Data Envelopment Analysis, and Analysis of Variance algorithms, developed in a machine learning environment. To further optimize discriminative power, the Particle Swarm Optimization Algorithm was included. By applying the PDA methodology to hospitals in the Apulia region of Italy, we found that facilities with better perceived quality tend to be more efficient. Furthermore, we identified significant variations in efficiency based on network affiliation and hospital level. Machine learning algorithms, including linear regressions and neural networks, confirmed that our methodology outperforms traditional Data Envelopment Analysis models in precisely distinguishing between varying levels of hospital efficiency. In conclusion, this work provides a fresh perspective for future research and policy decisions in the healthcare domain, demonstrating how the use of advanced and machine learning algorithms can result in more accurate evaluations and targeted interventions. [ABSTRACT FROM AUTHOR]

Strategic alliances and partnerships based on mergers and acquisitions can be used to improve the performance of decision-making units (DMUs). In this paper, we present different types of strategic alliance cooperation and partnership between DMUs and their corresponding aggregation DMUs. We propose two different strategies, we were able to carry out the process of strategic alliances and partnerships in semi-additive production technology. In the first strategy, two partner DMUs can compensate for the lack of resources in some of their input components by using excess input resources in some of each other's input components. Thus these DMUs can improve their efficiency score among all DMUs. The modeling of strategic alliances and partnerships in the first strategy was based on data envelopment analysis (DEA) models. In the second strategy, the two DMUs selected as partners adjust their input and output levels with the help of each other in such a way that these two DMUs achieve a target efficiency level predetermined by the DM. The modeling of strategic alliances and partnerships in the second strategy was based on inverse DEA models. We also apply the proposed approach to show importance strategic alliances and partnerships in banking. In the following, we compared the results of the models in carrying out the partnership process in two different technologies. The results showed that in semi-additive production technology, we can achieve potential gains for banks. [ABSTRACT FROM AUTHOR]

In the actual efficiency evaluation process, it frequently occurs that the quantitative input and output data of each decision-making unit is difficult to obtain due to the limitation in data measurement and the absence of prior information. Instead, the form of judgment information can be selected to express the input–output state of the evaluated decision-making units compared with the reference level. Traditional cross-efficiency evaluation methods do not involve this case and cannot be well used in this situation. Based on the above problems, a multiplicative cross-efficiency evaluation model with judgmental information is constructed, and the optimal adjustment model is provided by introducing deviation variables to deal with the inconsistent issue of data features. In addition, for the case of multiple optimal solutions, the maximizing and minimizing secondary objective models of the multiplicative cross-efficiency are established respectively to reasonably identify the corresponding optimal weights. Simultaneously, in order to effectively depict the interaction between decision-making units, the characteristic functions of JM-cross efficiency cooperative game and the multiplicative Shapley value are established based on benevolent and aggressive cross-efficiency matrices. Moreover, in order to avoid the extreme ranking results that are oriented by high impact decision-making units, a series of optimization coordination models are developed, and an optimal efficiency ranking vector generation method with strong applicability and decision flexibility is proposed. The optimal efficiency ranking vector not only creates a precise match with the given Shapley index of the multiplicative cross-efficiency game, but also bounds between the minimum and the maximum average efficiency. Finally, the validity and rationality of the discussed models are illustrated by a concrete example. [ABSTRACT FROM AUTHOR]

• An evaluation model is proposed by considering risk attitudes and aggregation method. • Our distance entropy function reduces the inconsistent degree of evaluation results. • Empirical example is performed to validate the applicability of the proposed model. • Sensitivity analysis is conducted to test the effect of parameter changes on results. Cross-efficiency evaluation with the data envelopment analysis (DEA) model is an effective way to assess performance and provide a complete ranking of decision-making units. However, it is generally assumed that decision makers are perfectly rational in the cross-efficiency model, which fails to consider the subjective preferences of decision-makers. Moreover, the arithmetic average method is usually adopted to aggregate efficiency scores in traditional cross-efficiency methods, which underestimates the importance of self-evaluation. To address these issues, we extend cross-efficiency with the DEA model by incorporating prospect theory and the distance entropy function. First, we calculate the prospect values of decision-making units to describe the non-rational subjective preferences under the risk of decision-makers. Second, based on prospect cross-efficiency, a new distance entropy function is developed to aggregate the ultimate prospect cross-efficiency values. More specifically, some traditional cross-efficiency evaluation models can be considered special cases of prospect cross-efficiency models with appropriate adjustments to the parameters. Finally, an empirical example is used to evaluate the prospect cross-efficiency results with the high-tech industries of 29 provinces in China to illustrate the applicability and effectiveness of the proposed model in ranking observations. [ABSTRACT FROM AUTHOR]

The institutions of world repute, like the United Nations, the World Bank, etc., assess and rank countries or firms based on various criteria pertaining to key areas to evaluate their performance and publish them as Indexes. These ranking tools aid decision-makers in formulating policies and procedures 4 4 PAP-Policies and procedures. (PAP) to achieve performance efficiency. In the real world, identifying the closest, most efficient country or firm whose PAP can be emulated with minimal effort is a management dilemma. These Indexes contain only output data, which compounds the problem. This paper uses Data envelopment analysis 5 5 DEA- Data envelopment analysis. (DEA) to resolve these issues. Each country or firm under evaluation is referred to as Decision-making unit 6 6 DMU-Decision-making unit. (DMU). The novelty of this study emanates from 1) segregating countries or firms into several frontiers using Pure Output Model-based Context-Dependent Data envelopment analysis 7 7 POMCDD-Pure output model-based context-dependent data envelopment analysis. (POMCDD), 2) proposing a technique to identify the most attractive nearest DMU that is performing better and is easy to emulate, 3) identifying the "Region of introspection" by using the translation technique of Pillar-Index scores concerning DMU under evaluation to demarcate reference DMU for the concerned Pillar, and 4) suggesting techniques such as "Rapid gain," "Best yield", and "Least effort" which are proposed to identify reference DMUs for the concerned Pillar. A case study analysis of the Logistics Performance Index 8 8 LPI-Logistics Performance Index. (LPI) is carried out to substantiate the contributions. The countries ranked in the Logistics Performance Index are segregated into several efficient frontiers using the POMCDD technique. The attractiveness of each country is evaluated relative to the frontiers of poorly performing countries. The translation of Pillar-Index scores concerning the country under evaluation is used to identify reference countries for that Pillar. The developed framework is helpful to the policy-makers in analyzing published Indexes. [ABSTRACT FROM AUTHOR]

Over recent decades, China's economy has continued to develop steadily and rapidly, contributing to a large environmental cost. Therefore, many researchers propose various methods to evaluate environmental efficiency to deal with these serious issues accurately. This paper is the first to evaluate environmental efficiency using a model featuring not only a parallel structure with three component processes (primary, secondary, and tertiary industries) in the first stage but also a sewage treatment process in the second stage. Simultaneously, we propose radial but also nonradial versions of the Environmental Multiple Variable Return to Scale (EMVRS) model. These two models both consider shared inputs as well as shared outputs, and the proportion of the shared inputs and shared outputs utilized in each component process is assumed to be unknown. Additionally, we further analyze the factors influencing environmental efficiency by utilizing the Tobit model. The results when EMVRS is applied to Chinese provinces can be divided into 3 parts: (1) Most provinces do not purify wastewater appropriately contributing to the bad environmental efficiency. (2) The efficiency values of the three industry types are similar or identical to each other in most provinces. (3) The population of people employed negatively impacts environmental efficiency. [ABSTRACT FROM AUTHOR]

In traditional multi-attribute group decision-making (MAGDM) methods, the aggregation of expert evaluation information, the normalization of attribute scales, and the determination of attribute prior weights can lead to information loss and inconsistent results. In addition, these methods cannot identify and improve the shortcomings of non-optimal alternatives. Considering these, this paper proposes a MAGDM method based on data envelopment analysis (DEA) by setting up a parallel expert evaluation system, thus integrating the initial opinions of all experts to rank the alternatives from a global perspective. First, we construct the multi-expert evaluation process as a parallel system from the global perspective so that the decision-making process is based on the initial expert evaluation information. Then, a new directional distance function model based on endogenous projection directions is proposed to measure the alternatives under each expert subsystem, eliminating data processing and artificial weight assignment. The non-optimal alternatives can be improved to an efficient state by adjusting the attribute levels to target values. Next, we propose a DDF super-efficiency model to perform a full ranking of the alternatives. Finally, a case study is provided to demonstrate the rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The variable returns to scale data envelopment analysis (VRS-DEA) benchmarking models are developed by assuming convexity on technology. However, this assumption may be not consistent with the classical microeconomic theory where marginal product initially increases but diminishing returns eventually set in. Accordingly, the selected benchmarks may aggregate best practices, which are less attainable in performance improvement. This paper thus proposes a VRS-DEA benchmarking model with quasiconcave production function, consistent with classical microeconomic theory. A novel approach for determining the returns to scale of Decision-Making Units (DMUs) is developed. The hyperplane of the quasiconcave production function is characterized. Benchmarks are selected by minimizing the weighted distance between the evaluated DMU and the hyperplane. An empirical application of China's tourism sector is used to validate the approach. The empirical results show that Mergers and Acquisitions are beneficial for smaller hotels as the production of most regions in China's tourism sector is subject to increasing returns to scale. [ABSTRACT FROM AUTHOR]

• A stochastic entropic analysis on the basis of ideal solutions is proposed. • The proposed model is applied to measure bank performance in Japan. • The proposed innovative model combines the advantages of DEA and TOPSIS. • Machine Learning methods are used to predict information gains. We propose a stochastic entropic analysis based on ideal solutions and apply it to evaluate the performance of Japanese banks. Not only is the odds ratio of the learning feedback modeled by Beta-distributed priors, but our innovative model also combines the advantages of Data Envelopment Analysis (DEA) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in performance evaluation. The information gains in terms of contextual variables are predicted using Machine Learning methods. Our findings demonstrate that our proposed method generates more comprehensive performance scores compared to DEA and TOPSIS. Additionally, we find that total liabilities, total assets, and total deposits, in that order, are the three most relevant inputs for understanding the outputs. Operating revenue, total income, and total loans are the output variables most pertinent to comprehending input behavior. We argue that while mutual information seems important in explaining efficiency variations, continuous improvement in the production process plays an even more crucial role in enhancing the performance of Japanese banks. [ABSTRACT FROM AUTHOR]

As an effective approach in ranking decision making units (DMUs), cross-efficiency evaluation is only extended to two basic network systems: series and parallel, which is not applicable to the common complex network system in reality. By using the directional distance function (DDF), this paper extends the cross-efficiency evaluation to a complex system called the parallel two-stage system, which consists of several independent parallel processes and each of them is a sub-system with a general two-stage series structure. The proposed method is able to decompose the cross-efficiency measure of the system into the weighted average of those of processes, stages and overall stages, respectively, and as a result, successfully achieves the layer-by-layer decomposition of cross-efficiency measures under complex nested networks. An empirical study on 40 open-ended equity funds in China from 2018 to 2020 verifies that the newly proposed method not only has a stronger performance discrimination power than self-evaluation, but also can identify the relationships among efficiencies of the system, processes and stages, and meanwhile it can reflect the effects of processes and stages on the system. More importantly, our method has a better practical value for actual fund investment selection. [ABSTRACT FROM AUTHOR]

With the rapid development of social informatization and platform economy, evaluating high-frequency and large-scale data has attracted widespread attention. One relevant and current research topic deals with finding high-quality decision-making units (DMUs) and scoring each DMU using a shorter computing time. The Framework method (Khezrimotlagh et al., 2019) has the fastest computing time so far. We propose a new parallel framework algorithm in this study. This algorithm effectively reduces the calculation time for finding all efficient DMUs and evaluate all DMUs in the BCC (Banker, Charnes and Cooper) model, the CCR (Charnes, Cooper and Rhodes) model, FG (Färe and Grosskopf) model and ST (Seiford and Thrall) model. The proposed algorithm has a reasonable selection method and optimal numbers of initial hull which can decrease the running time effectively. The extensive experimental results presented in this study show that when the input and output data are generated by uniform approach or normal approach, the density will increase with the increase of input and output dimensions, and show a growth trend like that of the S curve, which is similar to the distribution function of the Gamma distribution. We also tested our method using a real-life dataset. Compared with the previous methods, our method is shown to be more suitable for the input and output data generated by normal approach and decreases the calculation time by at least 12.52%. Furthermore, it has strong advantages in decreasing calculation time of solving the CCR model irrespective of whether the input data and output data are uniformly distributed or normally distributed. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) is a complex process involving numerous decision-makers (DMs). However, considering such a large number of DMs increases the complexity of the process. it seems necessary to pay much more attention to aspects such as a proper dimensionality reduction for scalability, consensus processes with automatic feedback, and effective management of non-cooperative DMs. To address such aspects, this paper presents a novel framework for LSGDM, based on Extended Comparative Linguistic Expressions With Symbolic Translation (ELICIT). We first extend the K-means clustering algorithm by incorporating individual assessments and trust relationships to classify DMs into subgroups, enhancing decision-making efficiency. We then develop a feedback mechanism based on two optimization consensus models for ELICIT information, that automatically provides optimal recommendations. An essential aspect of our proposal is the management of non-cooperative behaviors by utilizing the normal distribution to detect and penalize misbehaviors. Furthermore, we introduce a Data Envelopment Analysis (DEA) cross-efficiency method based on ELICIT values to rank all alternatives once an acceptable group consensus degree is reached. The framework's effectiveness is demonstrated through a practical application case study, accompanied by a parametric analysis. Comparisons with existing LSGDM methods highlight the superiority of our proposal in terms of efficiency. • A K-Means clustering algorithm based on ELICIT information and trust relationships is generalized. • A novel feedback mechanism based on two optimization models is built. • A novel approach for identifying and managing non-cooperative behavior is constructed. • An extended DEA cross-efficiency model for ranking alternatives is employed. • A novel large-scale group decision-making framework is developed. [ABSTRACT FROM AUTHOR]

• Identifying a common best practice frontier most similar to management goals. • The possible additional benefits generated from the global reallocation. • The stepwise reallocation paths for DMUs with limited reallocation potentials. • The case study based on 18 ports in Korea. Common benchmarking can identify a common best practice frontier as the benchmark to assess and improve the performance of Decision-Making Units (DMUs) experiencing similar circumstances. At present, further exploration in common benchmarking is still needed. For instance, the similarity between management goals and targets belonging to a common best practice frontier. To fill this gap, we propose the following innovative work: (1) A novel Data Envelopment Analysis (DEA) model is proposed to identify a common best practice frontier on which targets closest to management goals can be found for all DMUs experiencing similar circumstances. These targets need to be achieved through a global reallocation of inputs/outputs among DMUs; (2) Considering the targets closest to management goals are too ambitious for some DMUs, an iterative algorithm is given based on another new DEA model for providing the stepwise reallocation paths. The stepwise reallocation paths can distribute the total reallocation efforts into several intermediate steps; (3) The effectiveness of the proposed model and algorithm is illustrated by applying them to a case of 18 ports in Korea. For this purpose, we use the input–output data set of these ports in 2010 and assume actual management goals for every port. The results of the case show some conclusions. First, the target of each port located on the common best practice facet is closest to its management goal. Second, global redistribution can make all ports obtain the best input–output levels. Third, ports with limited reallocation potential can monotonically and gradually achieve their distant targets through our stepwise schemes. [ABSTRACT FROM AUTHOR]

While data availability provides adequate support for decision-making, asymmetrically distributed uncertainty remains an inevitable factor in the decision-making process. Robust data envelopment analysis (RDEA) is one of the most popular methods to handle uncertainty in input and output data. However, previous RDEA studies assume that the uncertainty of the data is symmetrically distributed, which is often violated in practical applications, such as in sports and finance. This paper aims to relax that assumption and extend RDEA models to more general scenarios. To this end, this paper proposes a new RDEA method that can capture asymmetrically distributed uncertainties from historical data and can be easily solved. In addition, a comprehensive case study of player efficiency evaluation is conducted to illustrate the merits of the proposed method. The contributions can be concluded as follows. This study introduces a novel approach to considering asymmetrically distributed uncertainty sets in the RDEA domain, which has not been previously explored. The proposed model outperforms existing RDEA models, providing more reliable and complete ranking results. Additionally, we demonstrate a comprehensive data-driven application that showcases how to build uncertainty sets from historical data and utilize our RDEA model for efficiency evaluation. Furthermore, our model's flexibility allows decision-makers to adjust its conservativeness according to their preferences, offering more dependable results. By utilizing our proposed model, decision-makers can make better-informed decisions while also receiving guidance on how to enhance the reliability of their results. [ABSTRACT FROM AUTHOR]

The traditional data envelopment analysis (DEA) method relies solely on current and historical performance and is unable to predict future efficiency. To overcome this limitation, we propose a novel prediction model called the input–output memory network (IOMN), which extends the Recurrent Neural Network (e.g., Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) to forecast the input and output variables in future periods. We then combined the IOMN with the DEA-Malmquist index to evaluate the efficiency of Decision-Making Units (DMUs) from the past to the future. To demonstrate the effectiveness of our proposed model, we conducted a case study of 15 research institutions in the Chinese Academy of Sciences (CAS) and compared its performance with those of linear regression (LR) and support vector regression (SVR) methods. The comparative results show that the IOMN significantly outperformed both the LR and SVR methods. Finally, we fed the past and predicted future data into the DEA-Malmquist index to examine the productivity efficiency trends in research institutions over time. Our results indicate that the annual mean of total factor productivity (TFP) continued to fluctuate at approximately 1 during the observation period. In the future, the geometric mean of TFP will decline slightly by 2.3%, the geometric mean of efficiency change (EC) will decline by 5%, and the geometric mean of technical change (TC) will increase by 2.9%. [ABSTRACT FROM AUTHOR]

In this paper we propose an alternative cross-efficiency aggregation scheme that is based on the theory underlying the aggregation of self-appraisal efficiency scores. For this purpose, we apply the denominator rule to aggregate the cross-efficiency scores obtained from the multiplier form of the DEA model. This results in a share-weighted average cross-efficiency that takes explicitly into account the relative importance of each element of the cross efficiency matrix by means of either the opportunity cost of resources used or the value of outputs produced, depending upon the orientation chosen to measure efficiency. It turns out that the proposed share-weighting average cross-efficiency uses the simple arithmetic average of the estimated input and output weights from the self-appraisal form of the DEA model to compute the ultimate cross-efficiency. As this is a set of common (across decision-making units) input and output weights, it allows for complete ranking and comparison of all (efficient and inefficient) decision-making units. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a crucial method for assessing the efficiency of decision-making units (DMUs). In practice, it is common to encounter situations where the performance of a DMU at a certain stage is evaluated, especially in policy effect evaluation. The key to stage performance evaluation is to eliminate the influence of pre-stage inputs (outputs) on stage performance. This paper proposes stage ratio DEA models that consider two types of inputs and outputs: accurate ratio data and interval ratio data. The first form considers the evaluation of stage efficiency when accurate ratios of inputs and outputs are used at the beginning and end of the stage. The second form assesses stage efficiency using interval ratio data for interval inputs and outputs. To verify the validity of the proposed models, the numerical example validates the first form of stage ratio DEA models. And the second form is applied to evaluate the sustainable efficiency of 14 energy saving and environmental protection clean enterprises (ESEPCEs). The empirical results demonstrate that the proposed models provide a more accurate assessment of stage efficiency compared to the traditional DEA-CCR model. [ABSTRACT FROM AUTHOR]

In the multi-period and multi-stage operational process of banks, fixed assets, deposits, and non-performing loans (NPLs) are elements with time lag effects. Not or only considering one time-lag element could result in biased efficiency evaluation results. Therefore, this study proposes a dynamic three-stage time-lag range directional measure (RDM) cross-efficiency model that considers three types of time-lag elements simultaneously to assess the operational performance of banks more accurately and comprehensively. Moreover, the proposed model takes into account the following factors: non-oriented, cross-efficiency, negative data, and undesirable elements. This study conducts in-depth research on the operational performance of Chinese commercial banks and obtains the following observations. (1) The change degrees of different types of banks influenced by the different time-lag elements were inconsistent. (2) Three time-lag elements significantly influenced the operational performance of four types of banks, which was most significant for state-owned commercial banks. (3) All banks were assessed as inefficient during 2017–2021, and the highest average efficiency was found in the capital allocation stage, while the profitability stage ranked at the bottom. (4) Rural and joint-stock commercial banks had the best performance in all sub-stages, while state-owned commercial banks had the worst performance. [ABSTRACT FROM AUTHOR]

Heterogeneous multi-attribute group decision making (HMAGDM) is a kind of complex decision making problem, which exists widely in reality. The purpose of this paper is to extend data envelopment analysis (DEA) for solving the HMAGDM problems whose attribute values include real numbers, intervals, intuitionistic fuzzy numbers, triangular fuzzy numbers, trapezoidal fuzzy numbers and linguistic variables. For this purpose, a method to HMAGDM based on a fuzzy DEA cross-efficiency model is proposed. In the proposed method, heterogeneous information is uniformly transformed into intervals, which are expressed as variables with unknown parameters. On this basis, a fuzzy DEA cross-efficiency model is established to calculate the individual cross-efficiency intervals of decision making units (DMUs). The model takes into consideration all possible attribute values of each DMU in the intervals, and obtains stable evaluation results through peer evaluation mode and iteration. Then, a maximizing group consensus model is established to calculate the weights of decision makers (DMs). Given the weights of DMs, the individual cross-efficiency intervals can be aggregated into the collective cross-efficiency interval, and DMU can be ranked based on this. An example of supplier selection is presented to illustrate the practicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

[Display omitted] Marginal rates of technical changes and marginal impact are useful tools to calculate the trade-offs between production factors in a production process. In the data envelopment analysis (DEA) framework, the calculation of these trade-offs (marginal rates of technical substitution, marginal rates of transformation, marginal productivity and marginal costs) using the existing deterministic approaches may be sensitive to uncertainty and variability of the input and output data. Therefore, in this contribution, we introduce a stochastic DEA model based on chance-constrained programming to develop a measure of the marginal rates of firms facing data uncertainty. In this contribution, chance-constrained programming is used to develop a procedure to calculate these trade-offs. Our proposed stochastic procedure is applied to sample data on 31 power plants. The empirical results on marginal rates obtained from our proposed stochastic programs revealed that the results are different at various tolerance levels of chance constraints. [ABSTRACT FROM AUTHOR]

Our study contributes to the empirical literature in two ways: 1) We propose an innovative Data Envelopment Analysis (DEA) model to estimate the cost efficiency of Chinese banks; 2) We propose a Stochastic Structural Relationship Programming (SSRP) Model based on neural networks to evaluate the interrelationships between efficiency and other bank-specific variables. The findings show that Chinese commercial banks experienced a gradual increase in the level of efficiency from 2010 up to 2015, after which the efficiency level experienced slight volatility and, by the end of 2018, ended up with an efficiency score of 0.746 on a scale of 0 to 1. We find that for banks with lower levels of efficiency, improvement in the efficiency level has a favorable effect on profitability, and these banks will also focus their business on traditional banking activities. Banks with higher levels of efficiency need to find alternative, profitable banking businesses in order to sustain their higher level of efficiency. [ABSTRACT FROM AUTHOR]

• The concept of value efficiency introduced in data envelopment analysis. • The novel algorithm proposed for calculating the value efficiency. • A new approach comes to incorporating preference information in the process of evaluating efficiency. • The value efficiency is calculated based on input and output components. • The method has been applied for real world data. Value efficiency analysis is an approach to incorporate the Decision-Maker's (DM) priori knowledge (preference information) into efficiency analysis. This paper presents a multi-objective programming model to measure the value efficiency. The model's feasible region is the production possibility set in data envelopment analysis (DEA) and the objective functions are the value efficiency scores corresponding to input and output components. Considering the structure of the proposed model and the concept of value efficiency, the step method (STEM) method to solve the model and gain the value efficiency is used in this paper. By selecting MPU (most preferred unit) units based on the manager's choice, the suitable benchmarks and value efficiency scores could be obtained corresponding to the unit under evaluation, then one could see that the obtained benchmarks and value efficiency scores are strongly dependent on the selected MPU units. Finally, a numerical example is proposed to illustrate the credit of the proposed approach. The method has been applied for real world data. [ABSTRACT FROM AUTHOR]

• Presents a novel fuzzy DEA based on a general fuzzy measure. • Develops an adjustable and flexible fuzzy DEA model to consider DMUs' preferences. • Applying the adjustable FDEA model for measuring efficiency of hospitals in USA. Possibilistic Data Envelopment Analysis (PDEA) is one of the most applicable and popular approaches in the literature to deal with imprecise and ambiguous data in DEA models. In this approach, with respect to tendency of decision maker (DM) in taking optimistic, pessimistic and compromise attitude, three measures including possibility, necessity and credibility measures are used to form the Fuzzy DEA (FDEA) models, respectively. However, decision makers may have different preference and so it is necessary to customize fuzzy DEA models according to properties of DMUs. This paper proposes a novel fuzzy DEA model based on general fuzzy measure in which the attitude of DMUs could be determined by the optimistic-pessimistic parameters. As a result, the proposed FDEA model is general, applicable, flexible, and adjustable based on each DMUs. A numerical example is used to explain the proposed approach while usefulness and applicability of this approach have been illustrated using a real data set to measure efficiency of 38 hospital in United States. [ABSTRACT FROM AUTHOR]

• Develop a fractal framework for virtual network optimisation and assessment; • Introduce the windows multiplicative DEA model in the presence of ratio data; • Show that devices on virtual networks have distinct fractal behaviour over time; • Prediction of a virtual setting with higher and stable TCP performance by long time; • The DEA results' dataset is available at URL: http://dx.doi.org/10.17632/776sjbz7z5.5. Recently, the prediction of the most efficient configuration of a vast set of devices used for mounting an optimised cloud computing services and virtual networks environments have attracted growing attention. This paper proposes a paradigm shift in modelling transmission control protocol (TCP) behaviour over time in virtual networks by using data envelopment analysis (DEA) models. Firstly, it proves that self-similarity with long-range dependency is presented differently in every network device. This study implements a novel fractal dimension concept on virtual networks for prediction, where this key index informs if the transport layer forwards services with smooth or jagged behaviour over time. Another substantial contribution is proving that virtual network devices have a distinct fractal memory, TCP bandwidth performance, and fractal dimension over time, presenting themselves as important factor for forecasting of spatiotemporal data. Thus, a continuous stepwise fractal performance evaluation framework methodology is developed as an expert system for virtual network assessment and performs a fractal analysis as a knowledge representation. In addition, due to the limitations of classical DEA models, the windows multiplicative data envelopment analysis (WMDEA) model is used to dynamically assess the fractal time series from virtual network hypervisors. For knowledge acquisition, 50 different virtual network hypervisors were appraised as decision-making units (DMU). Finally, this expert system also acts as a math hypervisor capable of determining the correct fractal pattern to follow when delivering TCP services in an optimised virtual network. [ABSTRACT FROM AUTHOR]

Founding on the design science research methodology (DSRM), this study integrates an additive network DEA model with hierarchical process to assess the R&D organization's two-stage production process model of "R&D performance" and "economic performance" and address the differences of cross-level organizations for decision analysis. With this proposed approach, we derive the overall composition and partition efficiency of the components and discuss the relationship between system efficiency, partition efficiency and composition of Taiwanese R&D organizations. The efficiency values of the economic stage have significantly increased, while the efficiency values of the R&D performance stage see no evident changes over the sample period. Moreover, the efficiency values of the economic stage are significantly better than the those of the R&D performance stage. On average, the sample R&D organizations still have 64.7% of room for improvement. Overall, the DSRM of this study provides an innovative "artefact" and new insights on the hierarchical process and network structure of national R&D organizations to help policy makers understand the competitive advantages and improve directions of R&D organizations and make the most effective decisions. [ABSTRACT FROM AUTHOR]

We consider the problem of ranking two-level efficient decision-making units (DMUs) in a hierarchical group structure. Traditionally, the radial hierarchical data envelopment analysis (DEA) models were used, but these are based on the assumption that there are proportional changes in inputs or outputs. To address this shortcoming, non-radial DEA models, capable of dealing with slacks directly are commonly used. To minimise the bias associated with using a single super-efficiency (SE) model, seven diversified SE models are extended to suit an environment with DMUs structured as hierarchical groups. The seven SE ratings of the suburb and district locations as potential locations of mobile money agents reveal wide variations. A proposed group technique for order preference by similarity to ideal solution (TOPSIS) ranking method is applied for harmonising the ratings and ensuring a complete ranking of efficient suburb locations. A complete ranking is achieved for all the district and suburb locations. The TOPSIS results are moderately well-verified and validated by the principal component analysis and Spearman correlation analysis methods. The accuracy of the district location TOPSIS rankings on the final suburb location TOPSIS rankings was discovered to be moderate at 61 percent. • Super-efficiency models extended to incorporate the hierarchical structure of DMUs. • Extended models applied on 16 district and 49 suburb locations. • Super-efficiency ratings combined with the TOPSIS method. • TOPSIS rankings verified with principal component analysis. • Segregation of efficient DMUs improved. [ABSTRACT FROM AUTHOR]

Determining the best way to measure level of customer satisfaction (LCS) with service quality and its determinants has been a matter of concern for both practitioners and researchers. This is especially so, as LCS can be used to retain customers, sell products and services, improve the quality and value of offers and ensure more efficient and economical operating. The present study proposes a new LCS evaluation methodology that combines data envelopment analysis (DEA) with text mining to analyze online textual reviews. The proposed methodology identifies, from online reviews using a term-frequency–inverse document frequency (TF-IDF) algorithm, multiple satisfaction metrics that significantly affect customers' service experience, quantifies them by sentiment analysis, and evaluates, by a DEA model, service providers' LCS with respect to those metrics. To illustrate the efficacy and applicability of the proposed approach, an empirical case study applying it to the world's top 20 airlines in 2020 was conducted. This study demonstrates how the DEA model can be effectively utilized for evaluation of LCS from online textual review data by combining it with the TF-IDF text-mining technique. [ABSTRACT FROM AUTHOR]

This paper develops an efficiency-based interactive dynamic technique to design the post-disaster rescue route by combining the data envelopment analysis (DEA), traveling salesman problem (TSP) and interval-valued hesitant fuzzy constraint cone (IVHFCC). The traditional DEA potentially presumes that the weights of the input (output) criteria are unrestricted, resulting in its inapplicability to some complex decision problems. To restrict the weights of input (output) criteria legitimately, this paper constructs an IVHFCC based on expert's subjective preferences in the form of interval-valued hesitant fuzzy sets. This paper defines the additive consistent normal interval-valued hesitant fuzzy preference relation (NIVHFPR) and analyses its desirable properties. Based on these properties, three linear programming models are proposed to repair the incomplete NIVHFPR, judge the additive consistency of the repaired NIVHFPR and generate the additive consistent NIVHFPR. By incorporating the TSP, DEA and the constructed IVHFCC, an efficiency-based interactive dynamic model is originally proposed to generate the optimal rescue route for the rescue team. Some desirable properties of this efficiency-based interactive dynamic model are analyzed. The validity of this model is demonstrated with a real post-disaster rescue route planning problem of Wenchuan earthquake. Some comparative analyses are provided to show the superiorities of the proposed technique. [ABSTRACT FROM AUTHOR]

The existing parallel DEA models assume that all decision making units (DMUs) should contain identical subsystems. This paper proposes a cross-efficiency evaluation method to handle the non-homogeneous DMUs (NHDMUs) in the parallel network system, where the non-homogeneity of DMUs is shown in that the input and output sets of subsystems and the internal structure of DMUs are not the same. The method considers the scenario wherein quantitative and qualitative data exist in the inputs and outputs simultaneously. To do this, first, the NHDMUs are divided into different mutually exclusive unit groups according to the subsystems in which DMUs participate. Then, several interval cross-efficiency models are constructed for the cross-efficiency evaluation of DMUs in the same group and the cross-efficiency evaluation of DMUs with overlapping internal outlines in different groups to obtain the interval cross-efficiencies of NHDMUs. Further, the evidence theory and grey relation analysis method are used to determine a set of weights that aggregate the cross-efficiency matrix. Finally, the proposed method is discussed using an application case of talent introduction performance. The results show that the proposed method can improve the rationality and distinguishability of efficiency evaluation and find the reasons for the performance decline of a DMU. [ABSTRACT FROM AUTHOR]

Assessing the technical efficiency of a set of observations requires that the associated data composed of inputs and outputs are perfectly known. If this is not the case, then biased estimates will likely be obtained. Data Envelopment Analysis (DEA) is one of the most extensively used mathematical models to estimate efficiency. It constructs a piecewise linear frontier against which all observations are compared. Since the frontier is empirically defined, any deviation resulting from low data quality (imperfect knowledge of data or IKD) may lead to efficiency under/overestimation. In this study, we model IKD and, then, apply the so-called Hit & Run procedure to randomly generate admissible observations, following some prespecified probability density functions. Sets used to model IKD limit the domain of data associated with each observation. Any point belonging to that domain is a candidate to figure out as the observation for efficiency assessment. Hence, this sampling procedure must run a sizable number of times (infinite, in theory) in such a way that it populates the whole sets. The DEA technique is used during the execution of each iteration to estimate bootstrapped efficiency scores for each observation. We use some scenarios to show that the proposed routine can outperform some of the available alternatives. We also explain how efficiency estimations can be used for statistical inference. An empirical case study based on the Portuguese public hospitals database (2013–2016) was addressed using the proposed method. [ABSTRACT FROM AUTHOR]

Operating rooms (ORs) are one of the most stressful and complex work environments, and the surgical staff are highly exposed to ergonomic problems. In addition, the satisfaction of the surgical staff is directly affected by these factors. In this paper, an integrated intelligent algorithm is introduced to evaluate the performance of ORs based on ergonomics indicators, quantitatively and qualitatively and to examine the impact of these indicators on job satisfaction. Initially, the required data was collected by distributing a standard questionnaire covering items associated with ergonomics and job satisfaction among the surgical staff of a mega-hospital. The study benefited from a combination of artificial neural network (ANN) and statistical methods to calculate the efficiency scores for each decision-making unit (DMU). The data envelopment analysis (DEA) method was used to validate the proposed algorithm. Afterward, sensitivity analysis and statistical tests were performed to evaluate the appropriateness of OR performance based on each of the ergonomics indicators. The results reveal that OR performance was appropriate based on knowledge, situation assessment, and situation analysis (KSS), specifications of work and equipment (SW), and mental power (MP). However, it was inappropriate in terms of information and communication system (IC), teamwork and effective communication (TE), physical condition (PC), and OR condition (OC). [ABSTRACT FROM AUTHOR]

This paper contributes by developing new models for assessing dynamic inefficiency that incorporate machine learning techniques. In particular, the new approaches apply decision trees models for the estimation of dynamic production technologies that account for investment adjustment costs. Methodologically, the new models build on the recently developed techniques of Efficiency Analysis Trees (EAT) and Convexified Efficiency Analysis Trees (CEAT) and extend them even further to a dynamic framework comprising dynamic EAT and CEAT models. The study compares dynamic inefficiency scores estimated assuming the new models against the traditional dynamic free disposal hull (FDH) and dynamic data envelopment analysis (DEA). Our empirical application focuses on dairy manufacturing firms in the main dairy processing countries in the European Union for the years 2014 and 2018. The results show that inefficiency related to the dynamic CEAT or EAT is higher than their corresponding values calculated through the dynamic DEA or FDH. The discriminating power of dynamic DEA (dynamic FDH) drastically improves when switching to dynamic CEAT (dynamic EAT). Finally, the differences between countries are observed regarding the development of dynamic inefficiency in the period associated with milk quota abolition. [ABSTRACT FROM AUTHOR]

Data Envelopment Analysis (DEA) is used for not only assessing the performance of a set of homogeneous decision-making units (DMUs), but finding the target operating points to improve inefficient DMUs. When inefficient DMUs are far from their target, it will be impossible to reach those in a single move, therefore, a strategy of gradual improvements with intermediate targets has been proposed. In this study, we propose a method to determine benchmarking paths, which consider the improvement steps as the learning of the production functions, not the learning of inputs and outputs. Under the constant returns-to-scale (CRS) assumption, the paper, for each inefficient DMU, sets the ultimate target as the closest target and determines the shortest improvement path with existing DMUs by Dijkstra's algorithm, and this is illustrated with a numerical experiment. As a real-world example, the method is introduced to the irons and steel enterprises in China, so that the specific steps to make efficient ones has been pointed out. The proposed method is the first study to discuss stepwise benchmarking path from the perspective of the production function. • The benchmarking paths are composed of existing DMUs. • The ultimate target is set as the closest efficient DMU. • The inefficient DMU learns the production functions of DMUs. • The distance between two production functions is calculated. • Dijkstra's algorithm is used to find the optimal benchmarking path. [ABSTRACT FROM AUTHOR]

This paper presents a new method for group decision-making (GDM) under multi-granular hesitant information based on the data envelopment analysis (DEA) cross-efficiency approach with regret theory (MGDM-RCE), which addresses some limitations of classic DEA for hesitant linguistic information, such as ignoring decision-makers (DMs)'s non-rational behavior and the use of single-granularity scales. The proposed MGDM-RCE method, on the foundation of cross-efficiency and regret theory with multi-granular hesitant fuzzy linguistic term sets (HFLTSs), allows for constructing two cross-efficiency models based on total regret-rejoice utility values to determine cross-efficiency intervals of decision-making units (DMUs). Additionally, an extended stochastic cross-efficiency technique is developed for obtaining the final ranking of alternatives in the correlative GDM problem. The performance of the MGDM-RCE method is shown using a numerical example consisting of selecting new energy sources and its validity and superiority are analyzed through sensitivity and comparative analysis. The sensitivity analysis revealed that variations in the parameters of the MGDM-RCE method do not significantly affect the ranking results. Moreover, compared to the classic methods VIKOR and TOPSIS, the MGDM-RCE method exhibits higher stability characteristics in addressing the GDM problem. [ABSTRACT FROM AUTHOR]

Assessing the efficiency of decision-making units (DMU) with fixed-sum outputs in a two-stage parallel-series structure is challenging for decision-makers. The two main challenges are: (1) constructing an equilibrium efficient frontier (EEF) in a two-stage parallel-series structure and, (2) handling multiple feasible EEFs. This study embeds the stochastic multi-criteria acceptability analysis (SMAA) method into a data envelopment analysis (DEA) model framework and proposes a two-stage parallel-series SMAA-DEA approach. This approach explores feasible EEFs and proposes decision-making indicators to obtain rank acceptability indices, efficiency dominance probability, and holistic rank acceptability for DMUs. Our approach's effectiveness and rationality are illustrated using a real dataset of 24 non-life insurance companies. The proposed approach provides reliable ranking results for DMUs and rich information to assist decision-making. [ABSTRACT FROM AUTHOR]

Transportation activities, especially road transportation, have a great impact on economic growth. On the other hand, sustainability is a major concern for transportation planning. In this work, a data-oriented network is developed to evaluate the sustainability of vehicle types. Then, this network is integrated with a multi-objective optimization model in order to provide the planning of a three-stage transportation problem, according to traffic congestion. Some criteria including total profit, efficiency of different vehicle types, relationship among the customers supplied by a specified retailer, risk of underestimating unmet demand, and selling price are used to determine the objective functions. The Chance-Constrained Programming (CCP) and Chebyshev Goal Programming (CGP) approaches are applied to solve the proposed integrated model. To the best of the authors' knowledge, it is the first time that traffic congestion under the conditions of simultaneous fuzzy and stochastic uncertainty has been integrated into sustainable transportation planning. In addition, the applicability and validity of the developed model are assessed on a case study. The results are then analyzed and appraised by Data Envelopment Analysis (DEA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The findings prove that the components of the proposed model have a very beneficial effect on the solution, and also perform much better than the competing approaches in the literature. Two important points from the results of this paper are that (a) traffic congestion is more effective in the initial levels of the supply chain, and (b) transportation planning using efficient vehicles may reduce the desirability of the objective function values. [ABSTRACT FROM AUTHOR]

Highlights • DMU ranking approach that synergistically combines different EIS techniques. • TOPSIS used to integrate second-order features extracted from raw input-output data. • In-strength, PageRank, inefficiency radius come from dominance network analysis. • Crowding distance, hypervolume, spacing indicators from multiobjective optimization. • The proposed approach has been applied to ranking major US airlines. Abstract This paper presents a new approach for ranking organizational units within a benchmarking context. Instead of the conventional optimization-based techniques, the proposed approach uses Social network analysis and Multiobjective optimization concepts to extract second-order features from the input-output data, integrating the resulting multidimensional information using TOPSIS. It shows how several expert and intelligent systems techniques can be harmoniously integrated and applied to performance assessment. The proposed approach has been used for ranking the performance of 27 major US airlines, comparing the results with some existing Data Envelopment Analysis methods. It is shown that the use of a richer information set instead of the raw input-output data leads to an innovative and more effective way of discriminating between efficient units. [ABSTRACT FROM AUTHOR]

Highlights • This paper studies the estimation of the right and left returns to scale. • The new method detects both the type and the value of returns to scale. • The advantage of the new method is that it uses no parameter in model formulation. • A case study in banking system validates the new method. Abstract One of the concepts that has gained important attention from economic and management-science research is that of the concept of returns to scale (RTS). There are several research papers on the behavior of the right and left RTS, as two specific directions of RTS, based on Data Envelopment Analysis (DEA) models. However, the main weakness of most of these methods is that they are based on the defined parameters, which itself leads to a high sensitivity of the models to variations in the magnitudes of the parameters and, thus, unreliable results. In this paper, we specially address this issue and propose a simple procedure for detecting the right and left RTS classification with an important feature that is independent of any predetermined parameters. In addition to the type of RTS, we also suggest a method to determine the value of the right and left RTS corresponding to each of the efficient DMUs. Finally, the superiority of the proposed parameter-free procedure over a parameter-based methods is elaborated by a real case in the banking system. [ABSTRACT FROM AUTHOR]

Highlights • We propose novel DEA efficiency scores based on a robust viewpoint. • The scores give a universal tool for comparing DMU's. • We show many remarkable properties of the scores and its interpretation. • We present extensions for uncertain data, additional DMU's and inputs/outputs. Abstract We propose a novel DEA method for computing efficiency scores. The method is based on a robust optimization viewpoint: the higher scores for those decision making units (DMU's) that remain efficient even for larger simultaneous and independent variations of all data and vice versa. Moreover, the value of each score itself gives the distance to inefficiency (or the distance to efficiency for inefficient units), so it provides a decision maker with an additional useful information on how stable the DMU is. The efficiency scores can be computed by solving generalized linear fractional programming problems, but we also present a tight linear programming approximation that preserves the order of rankings. We show many remarkable properties of our approach: It preserves the order of rankings compared to the classical approach, and it is unit invariant. It is naturally normalized, so it can be used for computing universal scores of DMU's of unrelated models. It gives scores not only for inefficient, but also for efficient decision making units. It can also be easily extended to generalized or alternative models, for instance to deal with interval data. We present several examples confirming the desirable properties of the method. [ABSTRACT FROM AUTHOR]

Highlights • A novel inverse problem in environmental efficiency is suggested. • A new inverse DEA model for minimizing GHG emissions is proposed. • For a set of firms and certain production, the model obtains minimum GHG emissions. • An application in oil and gas sector is proposed. Abstract The industry dynamic creates a difficult problem that must be solved if we are to address climate change. How can we lower greenhouse gas (GHG) emissions while simultaneously increasing production? This paper develops a new inverse data envelopment analysis (DEA) model for optimizing GHG emissions. The inverse DEA model minimizes the overall GHG emissions generated by a set of decision making units (DMUs) for producing a certain level of outputs, given that the DMUs maintain at least their existing performance status. The usefulness of the proposed method in this paper is demonstrated through an application in the oil and gas industry. We find that roughly 57% of our sample DMUs are inefficient. Ample room exists within the current efficiency frontier to lower GHG emissions in the oil and gas sector. We recommend that environmental regulators look into targeting regulations at these inefficiencies that exist within the industry as low bearing fruit for potential GHG emissions reductions. [ABSTRACT FROM AUTHOR]