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Measuring and improving the efficiency of a sustainable supply chain in an effective way for strategic decision making is important in the business world. In this article, an alternative coherent data envelopment analysis (CoDEA) with a representation of the intramural structure is established for evaluating the efficiency of a sustainable supply chain. The proposed CoDEA model not only maintains the traditional value of data envelopment analysis (DEA) in its “black box” approach, which avoids the intermediate measures among different nodes in the supply chain, but also overcomes some main pitfalls in previously developed DEA models. The usefulness of the proposed model is illustrated in both simple and complex supply chain situations, yielding reasonable efficiency scores as compared with those from the existing methods. Moreover, the dummy decision-making unit (DMU) introduced in CoDEA offers additional benefits in the scenario of a small supply chain. The approach allows the implicit rule that requires three times the ratio between the total measurement factors and total DMUs to be always satisfied, providing a higher discriminatory power of CoDEA. Based on the case studies covering both simple and complex supply chains, the results and sensitivity analysis ascertain that the proposed CoDEA model is a flexible and reasonable alternative with a good efficiency evaluation, contributing toward an efficient and sustainable supply chain management process pertaining to sustainable strategy or policy recommendations.

In this paper, we focus on a two-level production and distribution planning problem in the field of supply chain management, and examine the situations where the leader does not fully know the manufacturing technologies of the follower. In such a situation, the parameters representing the manufacturing technologies cannot be explicitly used to formulate the follower’s production planning problem. To overcome this difficulty, we propose formulations that implicitly express manufacturing technologies by using the input-output data observed from the production activities of the follower, incorporating the idea of data envelopment analysis (DEA). Assuming that the follower has multiple production facilities, we consider two possibilities of the observable input-output data and formulate two corresponding production planning problems; firstly, that only the input-output data aggregated for all the production facilities can be observed collectively, and secondly, that the input-output data for each of the production facilities can be observed separately. To clarify the validity of these DEA approaches, we compare them with the conventional formulation with technological coefficients using a numerical example.

In recent years, implementation of pay-for-performance (P4P) programmes in health care has become a worldwide initiative. However, most P4P programmes incorporate systems of Performance Indicators (PI) without accounting for trade-offs between indicators. This article has two objectives: (1) to develop a Data Envelopment Analysis (DEA) methodology for performance assessment of primary care providers; and (2) to propose an innovative methodology for P4P contracting based on the DEA assessment results. To achieve the first objective, we modify the standard DEA model to account for the necessary relations between the weights attributed to each PI and domain of performance and to account for the effect of the relevant environmental variables. To achieve the second objective, we combine relative and absolute performance assessments in the elaboration of several bases for reward, and then we link these assessments to a system of graduated rewards. A benchmarking programme is also proposed to contribute to the dissemination of best practices. This article contributes to the literature by proposing an enhanced methodology for performance assessment of primary care providers which can form the basis for P4P rewards planning. The applicability and advantages of the proposed methodology are illustrated with data from Portugal, but it can easily be adapted to different sets of PIs or domains, making it relevant for performance assessment and for P4P reward setting in other contexts and countries. info:eu-repo/semantics/publishedVersion

This paper introduces a modelling framework which combines Data Envelopment Analysis and Markov Chains into an integrated decision aid. Markov Chains are typically used in contexts where a system (e.g. staff profile in a large organisation) is at the start of the planning horizon in a given state, and the aim is to transform the system to a new state by the end of the horizon. The planning horizon can involve several steps and the system transits to a new state after each step. The transition probabilities from one step to the next are influenced by both organisational and external (non-organisational) factors. We develop our generic methodology using as a vehicle the homogeneous Markov manpower planning system. The paper recognizes a gap in existing Markovian manpower planning methods to handle stochasticity and optimization in a more tractable manner and puts forward an approach to harness the power of DEA to fill this gap. In this context, the Decision Maker (DM) can specify potential anticipated future outcomes (e.g. personnel flows) and then use DEA to identify additional feasible courses of action through convexity. These feasible strategies can be evaluated according to the DM's judgement over potential future states of nature and then employed to guide the organisation in making interventions that would affect transition probabilities to improve the probability of attaining the ultimate state desired for the system. The paper includes a numerical illustration of the suggested approach, including data from a manpower planning model previously addressed using classical Markov modelling.

This paper proposes an optimization method for energy planning that will efficiently meet multiple requirements subject to uncertain future projections. A stochastic optimization model is used to identify appropriate energy mixes under various scenarios of uncertainty, and the performance of three different energy policies—a pro-economic policy, a pro-environmental policy, and a governmental plan—is compared. Data envelopment analysis is applied to measure the relative energy efficiency of the optimized energy mixes in providing energy security, energy equity, and environmental sustainability. Thailand’s power development plan for 2032 is used as a case study to illustrate the approach. Analysis of the case study indicates that the pro-environmental policy is the most efficient of the three policies considered. Empirical results from this study provide quantitative support for policy makers seeking to establish an efficient energy policy to satisfy the three requirements while allowing for a range of future uncertainties.

Data envelopment analysis is a technique widely used to measure the relative efficiency of a set of decision making units and for ranking alternatives based on the measured efficiency. When there are data that need to be estimated, a group of experts is consulted. The opinions of the experts can be aggregated at either the data level, based on which the efficiency is calculated, or the efficiency level, where the efficiencies calculated from the data provided by individual experts are aggregated. However, the results may not be consistent, which leads to a puzzling situation as which result to follow. In this paper, a method of determining appropriate weights by which the opinions of the experts can be used to calculate the efficiency of alternatives is proposed. One radial model and one slacks-based measure (SBM) model are constructed based on this idea. It is shown that, for both models the results obtained from aggregating the opinions at the data level and at the efficiency level are the same. The final efficiencies are thus reliable. A case of a robot selection problem for a manufacturing company in Taiwan is used for the purpose of illustration. The results show that the radial efficiency is dependent on the non-Archimedean number and will overstate the performance of the robot. The SBM efficiency, which does not have this drawback, is more reliable than the radial efficiency to be used for ranking. A robot is suitably selected based on the SBM efficiency.

Within the framework of data envelopment analysis (DEA) methodology, the problem of determining the closest targets on the efficient frontier is receiving increased attention from both academics and practitioners. In the literature, the number of approaches to this problem are increasing, most of which are based on the computation of closest targets. Some of the existing approaches satisfy the important property of strong monotonicity. However, they tend to either propose a complex conceptual framework and multi-stage procedure or change the original definition of Holder distance functions. Clearly, these approaches cannot be solved easily when there are many “extreme” efficient units with multiple inputs and multiple outputs. To solve this problem, we consider the notion of the extended facet production possibility set (EFPPS). In particular, we propose a Mixed Integer Linear Program (MILP) to find the closest efficient targets and that is related to a measure that satisfies the strong monotonicity property. Additionally, in this paper, the proposed approach is applied to real data from 38 universities involved in China's 985 university project.

Classical efficiency studies on data envelopment analysis (DEA) consider all its inputs and outputs are desirable factors and real valued-data. Additionally, the DEA models only focus either on input-oriented projection minimizing inputs for an inefficient decision making unit (DMU) while keeping outputs at their maximum level, or output-oriented projection maximizing outputs under the present level of input consumption. To simultaneously deal with input excesses and output shortfalls maximizing both projections, this paper proposes a bi-objective DEA model in the context of undesirable factors and mixed integer requirements. These factors and requirements were integrated into the objective function and constraints of the existing bi-objective models. In addition, the proposed model estimates the returns to scale of DMUs that depends on the projections of input reduction and output augmentation. The applicability and usefulness of the proposed model were tested using the dataset of 39 Spanish airports retrieved from the literature. Besides, the proposed model was compared with the three existing bi-objective DEA models in the literature to test its validity.

Sustainable development has become a global tendency in the growth orientation of governments. Renewable energies such as wind and solar have attracted many concerns in reducing the level of fossil fuels dependence. One of the most significant stages in such a transmission process is determining the most suitable location. This article aims to propose a novelty approach combining two multiple criteria decision making models, namely data envelopment analysis (DEA) and analysis hierarchy process (AHP) to recommend the most optimal site for wind power plants. Vietnam has been one of the most successful ASEAN nations in attracting renewable energy investments; hence, the article considers it as a case study. In the first stage, there are five inputs and outputs conducted by DEA models to choose the five most potential alternatives among 12 locations for the next stage. In the second stage, the AHP model is implemented on five main criteria and 20 subcriteria that are referred to the options of experienced experts. The findings show that Ninh Thuan province, which is obtained with the highest final score (0.374), is the most suitable location for wind power plants construction. The article has contributed to renewable literature in terms of methodologies as well as to practice for both public and private stakeholders.

Radiotherapy treatment (RT) irradiates a patient's tumour volume while minimising damage to healthy tissue and surrounding critical organs at risk (OAR). In the conventional RT planning process, the RT planner has to iteratively adjust either the planning objectives (tumour or OAR dose levels) or the weights of the planning objectives until an acceptable plan is obtained that satisfies the minimum requirements. At the end of this iterative process, it remains unknown whether this plan is the best that can be obtained for the patient. The oncologist reviews each plan and decides to either treat using this plan or request further plan development, which may or may not lead to an actual improvement of the reviewed plan. We describe how Data Envelopment Analysis (DEA) is used as a real-time decision support tool to assess quality of RT plans for head and neck cancer patients by applying a knowledge-based comparison of each new plan to a library of previous clinically approved plans. This library allows benchmarking, which gives planners and oncologists a better idea of the relative quality of their plan and its improvement potential, resulting in improved use of resources and better quality treatments for patients. Our DEA-based approach provides a novel way of capturing multiple measures of plan quality as well as anatomical differences between patients in the benchmarking process. We present the developed DEA model and results for a set of benchmark instances. Initial results of integrating DEA-based quality feedback into the RT planning process are presented showing that operations research can contribute significantly to planning quality in this setting.

Data envelopment analysis (DEA) is a technique used to measure the relative efficiency of a set of production units that applies multiple inputs to produce multiple outputs. In its original settings, the data is required to be nonnegative. To allow for negative data, several methods have been proposed. While these methods have merits, they also have weaknesses and limitations. This paper generalizes the construction of the production possibility set from production units with nonnegative observations to those with real values. Given the signs of the aggregate target and aggregate observed outputs of the production units to be evaluated, different models are developed to calculate the efficiencies under both variable and constant returns to scale technologies, and an additive model is used to identify the signs of the aggregate target and aggregate observed outputs. Since the efficiencies are calculated from the original observations without transformation or manipulation, the proposed method does not have the drawbacks of the existing methods. A case of the Detroit National Bank shows that the results obtained from the proposed method are more representative and reliable as compared to those obtained from a data transformation method.

Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). In recent years, it has been widely used to evaluate two-stage systems under different organization mechanisms. This study modifies the conventional leader–follower DEA models for two-stage systems by considering the uncertainty of data. The dual deterministic linear models are first constructed from the stochastic CCR models under the assumption that all components of inputs, outputs, and intermediate products are related only with some basic stochastic factors, which follow continuous and symmetric distributions with nonnegative compact supports. The stochastic leader–follower DEA models are then developed for measuring the efficiencies of the two stages. The stochastic efficiency of the whole system can be uniquely decomposed into the product of the efficiencies of the two stages. Relationships between stochastic efficiencies from stochastic CCR and stochastic leader–follower DEA models are also discussed. An example of the commercial banks in China is considered using the proposed models under different risk levels.

In Data Envelopment Analysis (DEA) the situation of inputs vs. outputs is positioned as cause and effect. Effects include desirable (ordinary) outputs and undesirable outputs, e.g. pollutants. This situation is well studied and many applications have been published. In this paper, we introduce a new type of inputs, called Good (Desirable) Inputs. As explained in Introduction, we find several examples of such inputs, e.g. Electric car, Women in office and Test takers of vaccine. We handle this by means of SBM (Slacks-based Measure). Usually, efficiency values of DEA models are in the range (0, 1], while in this model a negative efficiency value may be assigned to inefficient DMUs (decision making units). This is caused by shortages of Good Input values. As an example, we refer to “Women’s Rights Movements” in a country where women’s right is not fully guaranteed. Suppose local governments where men and women are serving as officers. They are inputs to office, while Women are Desirable input and Men are Ordinary input. As outputs, we assume Service as Ordinary output and Claim as Undesirable output. Several extensions of this model are introduced. (a) Variable returns to scale, (b) Weight restrictions, (c) Super-efficiency issue and (d) SBM_Max model., https://www.grips.ac.jp/list/jp/facultyinfo/tone_kaoru/

Performance evaluation for universities or research institutions has become a hot topic in recent years. However, the previous works rarely investigate the multiple departments’ performance of a university, and especially, none of them consider the non-homogeneity among the universities’ departments. In this paper, we develop data envelopment analysis (DEA) models to evaluate the performance of general non-homogeneous decision making units (DMUs) with two-stage network structures and then apply them to a university in China. Specifically, the first stage is faculty research process, and the second stage is student research process. We first spit each DMU (i.e. department) into a combination of several mutually exclusive maximal input subgroups and output subgroups in terms of their homogeneity in both stages. Then an additive DEA model is proposed to evaluate the performance of the overall efficiency of the non-homogeneous DMUs with two-stage network structure. By analyzing the empirical results, some implications are provided to support the university to promote the research performance of each department as well as the whole university.

Data with large dimensions will bring various problems to the application of data envelopment analysis (DEA). In this study, we focus on a “big data” problem related to the considerably large dimensions of the input-output data. The four most widely used approaches to guide dimension reduction in DEA are compared via Monte Carlo simulation, including principal component analysis (PCA-DEA), which is based on the idea of aggregating input and output, efficiency contribution measurement (ECM), average efficiency measure (AEC), and regression-based detection (RB), which is based on the idea of variable selection. We compare the performance of these methods under different scenarios and a brand-new comparison benchmark for the simulation test. In addition, we discuss the effect of initial variable selection in RB for the first time. Based on the results, we offer guidelines that are more reliable on how to choose an appropriate method.

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA.

Congestion is an economic phenomenon of overinvestment that occurs when excessive inputs decrease the maximally possible outputs. Although decision-makers are unlikely to decrease outputs by increasing inputs, congestion is widespread in reality. Identifying and measuring congestion can help decision-makers detect the problem of overinvestment. This paper reviews the development of the concept of congestion in the framework of data envelopment analysis (DEA), which is a widely accepted method for identifying and measuring congestion. In this paper, six main congestion identification and measurement methods are analysed through several numerical examples. We investigate the ideas of these methods, the contributions compared with the previous methods, and the existing shortcomings. Based on our analysis, we conclude that existing congestion identification and measurement methods are still inadequate. Three problems are anticipated for further study: maintaining the consistency between congestion and overinvestment, considering joint weak disposability assumption between desirable outputs and undesirable outputs, and quantifying the degree of congestion.

One of the most important activities in any organization is the identification and selection of the right supplier. The responsible organizations determine the performance of their potential suppliers based on the attributes that are aligned with their sustainable development goals. Data regarding these attributes can be qualitative and/or quantitative, and not every supplier selection methodology can handle them simultaneously. Data envelopment analysis (DEA) is an influential methodology for measuring the suppliers' performance, especially when there are more than one input and/or output. However, the original DEA model cannot consider human judgment during evaluation. In this regard, the current study proposes a novel methodology with the aid of the Ordinal Priority Approach (OPA) of multiple attributes decision-making and the DEA. The proposed method, the DEA-OPA model, truly enjoys the advantages of both DEA and OPA models, making it more powerful than the original DEA for performance measurement. The proposed model was compared with the original DEA and OPA then retested through incomplete data when the experts lack enough knowledge about inputs and outputs. Finally, a pilot application has been executed in the paper industry, and comprehensive sensitivity analysis has been performed to illustrate the feasibility of the proposed model. The findings are of significant importance to responsible enterprises and organizational decision-makers.

Train dwell time is a complicated component and depends on many factors. One of the dominant factors is passenger volume. This study used actual train movement data and passenger demand data from London Underground, UK, to estimate the number of passengers and train dwell times at each station, and then evaluated train dwell times from a different perspective. Considering the various characteristics of stations, it is complicated to evaluate dwell time. Therefore, data envelopment analysis (DEA) was introduced to evaluate the dwell time at each station in relation to passenger volume at that station. The study investigated whether the dwell time spent at stations is efficient when considering the number of passengers that the stations can serve. The results showed that, in low-passenger-volume stations, the dwell time efficiency score is low and increases relative to the increase in passenger volume. For high-passenger-volume stations, interactions between passengers are more relevant and have a strong influence on dwell time. Passenger movement direction is a key factor to classify stations. This research proposes that stations should be classified according to their characteristics, and points out the challenge at any station with the same characteristics as Victoria station which has high passenger volume with bi-directional flow, and where trains arriving are crowded. This characteristic would result in high interactions between passengers, thus making a long dwell time. The station has to handle high passenger volume and also has to keep the dwell time within the threshold.

A city is a carrier of the development of human society; the higher the resilience of a city is, the better it can resist invasion from the outside. Therefore, the evaluation factors affecting resilient cities are of great practical significance in the study of resilient cities. The assessment method with hesitant fuzzy linguistic preference relation (HFLPR) has recently widely used in evaluation problems. However, some existing priority vector solving methods in the assessment method often ignore much decision-making information. Therefore, the aim of this paper is to present a new priority vector solving method for HFLPR by integrating data envelopment analysis (DEA) cross-efficiency and a distance-based priority aggregation (DPA) model. The innovation of this method contains: (1) DEA model is introduced to solve the priority vector of linguistic preference relation (LPR); (2) a DPA model is constructed to obtain the HFLPR’s priority vector, which can avoid the loss of decision-making information. For completely additive consistent LPR and incompletely additive consistent LPR, an output-oriented DEA model and DEA cross-efficiency model are introduced to derive its priority vector, respectively. An HFLPR is viewed as being composed of many LPRs, a DPA model is constructed based on the L2 norm to find the priority vector of HFLPR that minimizes the distance among the LPRs’ priority vectors. Based on the above, an integrated group decision-making method is proposed and then applied to an illustrative example of the evaluation factors affecting resilient cities to show its performance and advantages by comparing with the existing methods.

Purpose This paper aims to assess the efficiency levels of World Cup teams via the slack-based data envelopment analysis (DEA) approach, which contributes to filling an important gap for performance measurement in football. Design/methodology/approach This study focuses on a comparative analysis of the past two World Cups. The authors initially estimate the efficiency of the World Cup teams via the slack-based DEA approach, which is a novel approach for sports performance measurement. The authors also present the conventional DEA results to compare results. The authors also include improvement ratios, which provide significant details for inefficient countries to enhance their efficiency. Besides, the authors include effectiveness ratings to present a complete performance overview of the World Cup teams. Findings According to the analysis results of the slack-based DEA approach, titleholder Germany and France are found as efficient teams in the 2014 and 2018 World Cup, respectively. Besides, Belgium and Russia recorded the highest efficiency improvement in the 2018 World Cup. The novel approach for sports performance measurement, the slack-based DEA approach, significantly overlaps with the actual performance of teams. Originality/value This study presents novelty in football performance by adopting the slack-based DEA with an undesirable output model for the performance measurement of the World Cup teams. This empirical analysis would be a pioneer study measuring the performance of football teams via the slack-based DEA approach.

The parameter setting of meta-heuristic algorithms is one of the most effective issues in the performance of meta-heuristic algorithms and is usually done experimentally which is very time-consuming. In this research, a new hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis method and response surface methodology, called DSM. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the hybrid DSM method has been used to set the parameters of the cuckoo optimization algorithm to optimize the standard and experimental functions of Ackley and Rastrigin. In addition to standard functions, in order to evaluate the performance of the proposed method in real problems, the parameter of reverse logistics problem for COVID-19 waste management has been adjusted using the DSM method, and the results show better performance of the DSM method in terms of solution time, number of iterations, efficiency, and accuracy of the objective function compared to other.

Economic development in China requires lots of energy to support it, but how to acquire an adequate energy supply is a difficult problem. Meantime, environmental pollution caused by energy consumption is a problem that immediately needs to be solved. To adapt to China’s rapidly emerging economy, and based on existing policies, giving more consideration to energy saving and environmental safety is more important. Therefore, to investigate China’s regional environmental efficiency and its factors has key importance. In order to evaluate the environmental efficiency input in China, this study first selects some indexes of environmental efficiency and applies the Data Envelopment Analysis (DAE) method to measure the efficiency of input and output. Then, the relative index of environmental efficiency input is selected as the input variable and the efficiency value as the output variable. The Backpropagation neural network is employed to learn and establish the prediction model and achieve high prediction accuracy. The performance of the model is improved by optimizing the index of environmental efficiency investment, adopting the latest data, and increasing the learning samples. This method is not only suitable for the evaluation of macro-environmental efficiency investment, but also suitable for enterprises in specific industries.

PurposeProductivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.Design/methodology/approachAn integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.FindingsAnalysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.Originality/valueProposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

The paper deals with the technical efficiency evaluation under the fixed proportion technologies (FPTs) assumption where nonsubstitutable inputs and outputs are present. The recent research has shown that the conventional data envelopment analysis (DEA) models used for FPTs are biased, and when inputs and outputs are not substituted for either technological or other reasons, the models that account for nonsubstitutability need to be employed. This paper discusses a technical efficiency decomposition under FPTs and proposes two new models for quantification of contribution of various sources to overall inefficiency. The first model, called the calculation-based model, is a purely parametric model for efficiency decomposition, whereas the second model, called the adjusted CCR model, is a combination of parametric and nonparametric DEA approach. Using the models presented in the paper, the overall efficiency measure is decomposed into three main components: Farrell efficiency component, input mix efficiency component and output mix efficiency component. The selected estimators, some of them newly developed, are compared with the true efficiency levels via Monte Carlo simulations to measure the accuracy of the models. The results demonstrate that the proposed models provide sufficiently reliable results and in terms of overall efficiency are comparable with existing models for measuring efficiency in FTP conditions.

Data envelopment analysis (DEA) strives to evaluate the production units under their best conditions. DEA flexibility in selecting the appropriate input/output weights always results in unreal and zero weights. Treating decision-making units (DMUs) as black-box regardless of their internal structures misleads the DEA performance evaluation. While considering units as a network process, it is more likely to identify more inefficiency sources. This paper suggests using a new common set of weights (CSWs) approach to evaluate the units in both black-box and two-stage structures based on a unified criterion. Indeed, our contribution to this line of research is as follows: Firstly, we improve the model proposed by Kao and Hung (J Oper res Soc 56(10): 1196–1203, 2005) to calculate the CSWs in a linear-based optimization model. Secondly, a new CSWs method is suggested in the two-stage network DEA (NDEA) as multiple objectives fractional programming (MOFP) problem. Thirdly, the MOFP problem is converted into a single objective linear programming problem in the two-stage network case. Finally, an enlightening application is presented.

Under the effects of internal factors and the external environment, some customer requirements can be extremely sensitive to change; these are known as sensitive requirements. These requirements drive the identification and design of the relevant engineering characteristics. This study defines such characteristics as agile engineering characteristics (AECs), which are crucial for redesigning products and prolonging their service life but attract limited research and practice attention. The research question of identifying the product AECs is answered in this study, and three key issues are solved in the proposed methodology. First, the contribution of product engineering characteristics to customer requirements is calculated using an orthogonal experimental design, and the optimal level combination of engineering characteristics to satisfy the customer requirements is determined through the analysis of variation. Subsequently, the relative importance of engineering characteristics is calculated based on data envelopment analysis. Next, an agility index is defined by integrating the customer satisfaction degree, the relative importance degree of engineering characteristics, and the variation of the level of engineering characteristics, to identify the AECs. Finally, based on the identification of the AECs of an electric vehicle air-conditioning heat pump scroll compressor, the results are analysed and compared to illustrate the feasibility and effectiveness of the proposed method.

Purpose: The aim of the article is to present the results of efficiency studies using the Data Envelopment Analysis (DEA) method, as well as the Tobit model using examples of 29 public vocational universities operating in Poland. Design/Methodology/Approach: The research methodology is the DEA model in variants CCR-0, CCR-I, BCC-0, BCC-I. These are models based on the radial reduction of inputs in an input-oriented model. Taking into account the degree of differentiation of ineffective DMUs, it was decided to use the SE CCR-I model. Continuing the analysis and drawing conclusions from the above-mentioned DEA methods, the Tobit model was used in the next stage. Findings: From the point of view of the aim of the research, the SE DEA CCR-I model turned out to be the most useful. First, thanks to the removal of the limitation related to the maximum value of the evaluation of the efficiency of the studied DMU, a broader and more precise characterization of the efficient facilities was obtained. It became possible to distinguish among facilities classified as efficient, going beyond the mere statement that in their case there is efficiency at level 1. Second, the model was not so much focused on differentiating inefficient facilities, showing a picture of facilities with efficiency scores above 1, without compromising the quality of evaluating facilities with efficiency below 1. Practical Implications: The DEA method basically allows one to determine whether the object studied converts its expenditures into results in an optimal way, i.e., it allows for determining the effectiveness of the object under study. Determining the effectiveness of the functioning of public vocational universities is important for the future of this sector. Originality/value: The application of the DEA method to research the effectiveness of universities is not as often used as in the case of entities operating for profit. This is due to the complexity of the selection of inputs and outputs. The most common analyses using DEA methods relate to academic universities. Hence the need to extend such research to public vocational universities, that matter in the transfer of human resources to the labor market, is very important., peer-reviewed

Data envelopment analysis (DEA) is a useful tool for measuring the relative efficiencies of participating nations in the Olympic Games. DEA models with restricted multipliers have been used to refine efficiency evaluations by imposing additional information. Existing DEA models for evaluating Olympic medals do not focus on multiplier restrictions regarding input. To fill this research gap, this study incorporates a data fitting technique of medal prediction using ordinary least squares regression in input multiplier restrictions of the conventional DEA model. We show that the efficiency of the proposed model can be decomposed into the achievement ratio of substantial medal total and the unit value index of medals. Such decompositions can be used to analyze the effectiveness of host nations and athlete development initiatives. For an illustrative empirical application, we examine the target that the Brazilian Olympic Committee (BOC) set for the 2016 Summer Olympic Games (Rio 2016). Our results explain the extremely high feasibility of Brazil’s target of being in the top 10 medals table in Rio 2016.

Ghasemi, Ignatius, and Rezaee (2019) (Improving discriminating power in data envelopment models based on deviation variables framework. European Journal of Operational Research 278, 442– 447) propose a procedure for ranking efficient units in data envelopment analysis (DEA) based on the deviation variables framework. They claim that their procedure improves the discriminating power of DEA and can be an alternative to the super-efficiency model that is well-known to have the infeasibility problem and the cross-efficiency approach which suffers from the presence of multiple optimal solutions. However, we demonstrate, in this short note, that their procedure is developed based upon inappropriate use of deviation variables which leads to the development of a ranking approach that does not meet their expectations and as a result, an unreasonable ranking of decision making units (DMUs). We also show that the use of deviation variables, if interpreted and used correctly, can lead to developing a cross-inefficiency matrix and approach.

One effective way to achieve emission reduction targets is to allocate overall emission reduction tasks among regions. However, existing AEP optimization models do not consider technology heterogeneity between regions. This study addresses this problem, by first incorporating a meta-frontier technique into the data envelope analysis model (DEA) to measure the level of energy conservation and emission reduction (ECER) technology of different regions in China. Then, the study proposes an optimization model for emission reduction task sharing, by integrating DEA and ECER technology. Compared with previous models, the optimization model proposed in this study considers both technology and efficiency factors. The proposed model was applied to an empirical analysis of 176 cities in China from 2012 to 2016. The empirical results show that the average comprehensive efficiency of all the sample cities is very low. This indicates there is great potential for improving the environmental performance of Chinese cities. The environmental performance results of the sample cities further verify the Kuznets hypothesis: environmental performance and economic development level follow a U-shaped curve. ECER technology levels in China's third- and fourth-tier cities have not significantly changed in recent years. There is an increased reduction in sulfur dioxide (SO2) emissions in Chinese cities, but dust emission reduction is unstable, especially in the third-tier cities. Based on these results, this article also proposes a series of policy recommendations for cities to improve ECER performance.

Purpose University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation. Design/methodology/approach This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs. Findings Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score. Originality/value This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

In general, each country continuously focuses on improving the performance of the education system. Our study provides suggestions regarding the improvement by taking the data in the Indian context using data envelopment analysis (DEA). To the best of our knowledge, our paper is the first study which is using the National Institutional Ranking Framework (NIRF) data. In this study, we consider 61 educational institutions of India to rank them according to their performances. The ranking is done on the basis of the efficiency scores of the institutions. To rank the efficient institutions super-efficiency DEA model is applied. Finally, some suggestions and concluding remarks are given for inefficient institutions to make them efficient.

Allocating a fixed cost among a set of peer decision-making units (DMUs) is one of the most important applications of data envelopment analysis. However, almost all existing studies have addressed the fixed cost allocation (FCA) problem within a traditional framework while ignoring the existence of undesirable outputs. Undesirable outputs are neither scarce in various production activities in real world applications nor trivial in efficiency evaluation and subsequent decision making. Motivated by this observation, this article attempts to explicitly extend the traditional FCA problem to situations in which DMUs are necessarily involved with undesirable outputs. To this end, we first investigate the efficiency evaluation of DMUs considering undesirable outputs based on the joint weak disposability assumption. Then, flexible FCA schemes are considered to revisit the efficiency evaluation process. The results show that feasible allocation schemes exist such that all DMUs can be simultaneously efficient. Furthermore, we define the comprehensive satisfaction degree and develop a satisfaction degree bargaining game approach to determine a unique FCA scheme. Finally, the proposed approach is tested with an empirical study of banking activities based on real conditions.

The design-build project delivery method allows owners to deal with a single source of responsibility for the design and construction phases of a facility. Due to its inherent complexity, there is ...

We develop a new network data envelopment analysis (DEA) approach for two-stage network systems considering a match between the production scale of the substages and the intermediate measure levels. Several explicit production axioms are introduced to build a production possibility set. New models are developed based on the production possibility set and a frontier projection procedure with the production scale matching process. Unlike the existing approach which assumes the intermediate measures are free-setting decision variables, the new envelopment network DEA models project the intermediate measures of a unit using the radial projection technique. Correspondingly, the resulting multiplier network DEA models allow for weight flexibility on the intermediate measures while holding a total value flow equivalence between a unit's two stages. We show that our approach does not suffer the known network DEA pitfalls. It identifies the overall efficiency, divisional efficiencies, and frontier projection using either an envelopment or a multiplier network DEA model, i.e., the primal-dual correspondence holds in our approach. Our approach also avoids uncertainties in determining divisional efficiencies by generating a unique pair of divisional efficiencies for each unit. Additionally, the adoption of the production scale matching process explains clearly the frontier projection procedure from the practical point of view. The proposed approach is illustrated with a numerical example and compared with the existing approaches with a case study of commercial bank branches.

Grasping the intricacy and diversity of complex systems dealing with ever-growing amounts of data is essential to public and private institutions’ continuous improvement. Composite indicators (CIs) emerge as aggregators of key performance indicators, providing a single measure that reflects those multidimensional performance aspects. One way to build such measures is based on the use of data envelopment analysis (DEA). Several DEA models can be used to generate CIs. Still, not many of them can deal concurrently with desirable and undesirable outputs, and incorporate the decision-making actors’ preference information. Based on the directional ‘Benefit-of-the-Doubt’ model, we propose a novel approach consisting of the simultaneous use of weight restrictions and an artificial target reached via a range directional vector. The resulting CI assesses the Portuguese public hospitals’ performance under two perspectives of hospital activity: users and providers. In the end, managerial and policy implications are withdrawn from the results of this study conducted in cooperation with the Portuguese Ministry of Health.

The literature on data envelopment analysis (DEA) and, more broadly, production theory employs different notions of efficiency for the characterization of boundary points of production technologies. These include the strong and weak (Pareto) efficiency, and the Farrell input and output efficiency. For the conventional constant and variable returns-to-scale technologies, the relationship between the different notions of efficiency has been explored in the literature and is well understood now. In this paper we show that, in the general case, which includes many recently developed technologies, the conventional relationship between the different notions of efficiency is no longer valid. We show that such relationship depends on the properties of a particular technology such as convexity, disposability and returns-to-scale characteristics. Our results are applicable to many new technologies for which the different notions of efficiency and methods of their testing have not been fully explored.

PurposeThis study extends the educational institutions' performance and efficiency literature by examining Brazil's Federal Institute of Education, Science and Technology (FIEST), which consists of educational units throughout the country that span several levels of education.Design/methodology/approachThe authors build and analyze a covariance matrix consisting of both a group of efficiency measures and a group of performance indicators used by Brazil's Ministry of Education (BME). The values in the covariance matrix are maximized through application of the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS), in which the weights of each variable are optimized in order to capture the direction of the relationship between the two sets of efficiency measures.FindingsAlthough the authors find that the collective efficiency of the educational units analyzed did not change during the period of study, the analysis reveals that government indicators of performance do not exhibit a strong relationship to the ideal solution efficiency measures used in this study.Originality/valueThis study extends the educational institution efficiency literature by examining Brazil's FIEST, which consists of 40 educational units throughout the country that spans several levels of education, from upper high school vocational courses to higher degrees.

Objective:The purpose of this study is to investigate the efficiency of the Iranian Red Crescent Society (IRCS) in managing their nonmonetary resources involved in coronavirus disease 2019 (COVID-19) response.Methods:For this purpose, the data envelopment analysis approach was used to measure the efficiency, considering the number of personnel and vehicles and screened passengers as the input and output parameters, respectively. It was examined the efficiency of 10 IRCS’s branches given 17 d of screening operation. For the analysis, the DEA SolverPro software 15a version was used.Results:The results show that only 1 branch had been fully efficient in using the resources, while 5 branches showed less than 50% efficiency. This study reveals that it is unnecessary to use a fixed number of volunteers at different stations with different passenger numbers.Conclusions:Using resources without efficient planning can lead to direct costs such as food, transportation, and maintenance, as well as indirect costs such as burnout, fatigue, and stress when responding to the COVID-19 pandemic. This analysis should support IRCS’s managers to move their valuable resources from inefficient to efficient centers to increase the screening rate and reduce the fatigue of aid workers for the next pandemic rounds.

Efficiency, especially in the health care sector, has been a topical issue. The examination of the health care efficiency shows the extent to which the health care system inputs are efficiently used to provide health care services outputs. The purpose of the study is to analyse technical activities of twelve departments in a private hospital in the Slovak Republic. The data used were obtained from the statistical databases of 2019 – 2020. Activities were evaluated by using the Data Envelopment Analysis (DEA). DEA is a non-parametric method, which was designed to evaluate efficiency of production units. DEA can be used to analyse a large number of inputs and outputs with a multicriteria evaluation of variants. The BCC and CCR models, which aim to minimize inputs, were employed. In the empirical part of the study, DEA was utilised to identify technical efficiency of the hospital wards. In addition, the efficiency of transforming inputs into outputs using efficiency scores is compared and input optimizing options are given. In 2019 and 2020, the Department of Neonatology and Department of Surgery reported the best efficiency scores in both models. Following the analysis, reference rates for inefficient departments are given. In the next period, the hospital management is recommended to carry out continuous evaluations of the indicators by the model used for the needs of operational management.

Choosing an optimized Internet network by the users and providing a desired network by the internet service providers have always been big challenges in this field. Although there are different approaches for selecting the best set of networks such as Analytic Hierarchy Process, Analytic Network Process, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), choosing a unique optimal solution still remains an open challenge. The purpose of this paper is to use the decision-making techniques of Data Envelopment Analysis in order to evaluate the existing Internet networks so as to select the most desirable networks. Firstly, a specific Internet network called differentiated service network, that provides the quality of service to the user through the mechanism of Call Admission Control, is stimulated. A novel cross-efficiency model is proposed in order to provide a unique ranking of the Internet networks so as to select the optimal network. In particular, secondary goal model based on a satisfaction rate in cross-efficiency is proposed to evaluate and uniquely rank the Internet networks and to select the most desirable network. The present simulated results of 33 networks demonstrate that the proposed model is an effective method for unique ranking of the networks.

Purpose Project selection management is a matter of challenge for project-oriented organizations, particularly, if the decision-makers are confronted with limited resources. One of the main concerns is selecting an optimal subset that can successfully satisfy the requirements of the organization providing enough resources to the best subset of the project. The projects for which there are not enough resources or those requiring whole resources of the organization will collapse soon after failed to success. Therefore, the issue is in the risk of choosing a set of projects so that can make a balance in investment versus on collective benefit. Design/methodology/approach A model is presented for project selection and has been tested on the 37 available projects. This model could increase the efficiency of the whole subset of the project significantly in comparison to the other model and it was because of choosing a diverse subset of projects. Findings Provides a general framework for project selection and a diverse and balanced subset of projects to increase the efficiency of the selected subset. Also, reduces the impact of uncertainty risk on the project selection process. Research limitations/implications For the purposes of project selection, any project whose results are uncertain is a risky project because, if the project fails, it will reduce combined project value. For example, a pharmaceutical company’s R&D project is affected by the uncertain results of a specific compound. If the company invests in different compounds, a failure with one will be offset by a good result on another. Therefore, with selecting a diverse set of projects, this paper will have a different set of risks. Originality/value This paper discusses the risk of selecting or being responsible for selecting a project under uncertainty. Most of the projects in the field of project selection generally consider the risks facing the projects or existing models that do not take into account the risk.

PurposeThis paper presents a developed spatial-temporal analysis framework for the case of investigating the business performance of construction consultancy services (CCS) in China.Design/methodology/approachThe spatial-temporal analysis is based on the data envelopment analysis (DEA) technique. The spatial analysis follows the DEA results under a contemporaneous benchmark technology and a virtual decision-making unit, consisting of ranking analysis, cluster analysis and variation analysis. The temporal analysis is reliant on the DEA results under a global benchmark technology and the time value of money, including trend analysis and driving force analysis containing pure technical and scale efficiency factors.FindingsThree CCS types in China are investigated, including engineering survey and design, construction supervision and procurement agency. The performance rank order and cluster classifications are mainly related to economic development levels. Engineering survey and design demonstrates the best performance and higher imbalances; however, construction supervision and procurement agency illustrate lower performance and imbalances. Scale efficiency significantly promotes business performance, whereas pure technical efficiency plays an inconspicuous role.Practical implicationsThe CCS promote technical efficiency by developing their service and innovation levels. The service of engineering survey and design registered in Beijing, Shanghai and Guangdong is recommended for entering the service market in China.Originality/valueThe spatial-temporal analysis framework was developed, which is generic and provides a pathway to measure, compare and assess performance comprehensively. The CCS business performance is firstly measured.

The paper aims at the evaluation of efficiency in sports. Many articles are dealing with the application of data envelopment analysis (DEA) models in this area. They are mainly oriented on efficiency evaluation of teams and not the individual players. On the contrary, the main aim of this paper is to combine both approaches and investigate the relation between individual efficiency of the players and the efficiency of the teams. The first step is the evaluation of individual efficiencies, and the second one is its aggregation into the teams' performance within a competition (League). The idea is to evaluate the efficiency of individual players in certain positions and explore how the individual efficiencies contribute to the efficiency of the teams. Individual efficiency is measured using traditional radial and slacks-based measure DEA models. Team efficiency is derived in several ways—traditional DEA models with the variables describing the true achievements of the teams, parallel DEA models that consider all positions and players, and actual results of the teams in the League, which is the true performance of the team. The study is based on the Canadian-American National Hockey League (NHL) statistics in 2019/2020. The results of the analysis are compared and discussed. They show that the true performance of the team is not always directly dependent on individual performances of the members of the team.

Merging decision-making units (DMUs) is one of the most important issues in data envelopment analysis (DEA). Hitherto, several merging approaches have been presented in DEA; however, none of them can be used in network DEA. Because they do not consider intermediate products of two-stage DMUs (or two-stage processes) in the merging process. To tackle this problem, this study contributes to network DEA by introducing a novel merging approach. In this approach, we first survey the situations of the first and second stages of the candidate two-stage DMUs relative to the efficient frontiers and then obtain the merged two-stage DMU based on these situations. In other words, our proposed approach estimates the appropriate inputs and intermediate products for merging the candidate two-stage DMUs so that the merged two-stage DMU gets its favorable efficiency score. This research also explains the managerial and economic implications of merging two-stage DMUs. Finally, a numerical example and an empirical application to the US commercial banks are provided to show the use of the proposed approach.

In this paper, we propose a flexible two-stage data envelopment analysis (DEA) approach to evaluate the bank performance. Specifically, instead of fixing the role of the deposits in an ex-ante manner, the proposed approach regards deposits as a flexible measure in which it can play different roles for different banks under evaluation. Further, the traditional two-stage approach that regards deposits as an intermediate measure can be a special case of our proposed approach. Additionally, a potential Pareto efficiency improvement for multiple perspectives is identified, which can mitigate discontentment arisen from those fixed-role strategies. The applicability and superiority of the proposed approach is illustrated by assessing the performance of Chinese listed banks over the period from 2014 to 2018. The empirical results demonstrate consistent evidence that the inefficiency of the banking system in China is mainly sourced from the value-added stage. However, different banks may prefer to clarify different roles for the deposits, demonstrating the importance of employing the proposed flexible approach.

Cooperation is an important strategy to improve companies’ profitability. As a data-driven tool for performance evaluation, data envelopment analysis (DEA) is often used to measure the benefits fro...