Common Weight Data Envelopment Analysis Research Articles

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

Common-weights fuzzy DEA model in the presence of undesirable outputs with ideal and anti-ideal points: development and prospects

AbstractIn the literature, fuzzy arithmetic approach has been used for solving fuzzy data envelopment analysis (DEA) model in the presence of undesirable outputs by use of ideal and anti-ideal decision-making units (DMUs). In order to obtain the best and the worst fuzzy efficiencies, such an approach allows each DMU to choose the most favorable weights via optimizing its own evaluation that needs to solve various mathematical models. On the one hand, solving one mathematical model to find the best fuzzy efficiency and solving another mathematical model to find the worst fuzzy efficiency for each DMU increase the computational complexity significantly. On the other hand, the fully flexibility of weights leads to the different set of weights that may not be desirable. This paper proposes a common-weight method from two optimistic and pessimistic perspectives in fuzzy environment to determine the common sets of weights (CWS) to compute the best and the worst fuzzy efficiencies of all DMUs. The advantages of using common-weight DEA method based upon fuzzy arithmetic approach are reduction of the computational complexities and evaluation of equitably the best and the worst fuzzy efficiencies on the same base. The proposed approach first solves one linear programming model to compute each of the best and the worst fuzzy efficiencies of all DMUS, and then combines them to find a relative closeness index for the overall assessment. The developed approach is illustrated by a numerical example form the literature and the obtained results are compared with those from the existing ones.

Combining common-weights DEA window with the Malmquist index: A case of China’s iron and steel industry

In the conventional data envelopment analysis (DEA) window analysis, a decision-making unit (DMU) in each window is treated as different units in each period so that the evaluation for one unit is performed on different scales over time. This paper proposes a novel window analysis based on common weight across time (CWAT), which evaluates each unit in each window by its common scale independent of time. The model for obtaining common weights is described as linear programming. And the paper suggests the Malmquist productivity index (MPI) on CWAT, CWAT MPI, to analyze productivity change by inheriting the result of window analysis. The numerical experiments are illustrated to examine the validity of CWAT and MPI, and the result shows that the proposed method provides a new evaluation scale compared to previous studies. The proposed model is applied to evaluate the performance of China 45 iron and steel enterprises during 2009–2017. The energy and environmental efficiency are calculated using CWAT, and CWAT MPI analyzes the productivity change.

Evaluating sustainable efficiency of decision-making units considering undesirable outputs: an application to airline using integrated multi-objective DEA-TOPSIS.

Sustainable development has gained significant attention in the literature due to the increased global awareness of environmental sustainability during the last decade. Sustainable development has three aspects, including economic, social, and environmental. The challenge of sustainable development is to establish a balance between these three aspects. Assessing the efficiency of a company contributes comprehensive information to improve its overall performance. Despite numerous studies in this field, the literature lacks studies that simultaneously consider all three aspects of sustainable development, especially the social aspect. The main objective of this paper is to calculate the technical, social, and environmental efficiency scores. We also introduce a new efficiency called sustainable efficiency that merges all three sustainable development aspects in one efficiency score. This study applies two existing data envelopment analysis (DEA) models to evaluate technical, social, environmental, and sustainable efficiencies. These models, namely the three-step method and the modified three-step method, are computationally intensive. Also, this paper introduces two new DEA models, namely the common weight goal programming DEA and the common weight DEA, to assess the efficiencies with much fewer computations. Each model produces results that are different from one another. Therefore, the TOPSIS approach is applied to provide an overall result by integrating the results obtained from the four presented models. For this purpose, the implementation of four TOPSIS models is required. To illustrate the capability and validity of the developed models in efficiency calculation, a case of Iranian airlines is presented. The selected airlines are evaluated in different aspects, and final results are obtained by applying TOPSIS. The findings show that using TOPSIS to combine the results of several DEA models leads to a fully ranking of airlines in four aspects of technical, social, environmental, and sustainable efficiencies. Also, it is recommended to managers to probe pairwise comparison between different efficiencies of airlines in order to find and improve the weak ones.

A two-stage multi-objective second generation biodiesel supply chain design considering social sustainability: A case study

Undesirable environmental impacts caused by fossil fuel consumption have motivated governments to focus on biofuels production. Therefore, in our paper, we design a multi-objective biodiesel supply chain from Jatropha, which includes biomass cultivation center, pretreatment, and oil extraction center, biorefinery, and demand zone. In our study, a two-stage approach presented, which first specifies candidate locations for biomass cultivation with a common weight data envelopment analysis (CWDEA) method and then strategic and tactical decisions were made by a mathematical model. The presented multi-objective model focuses on cost minimization, besides social benefits and environmental impacts maximization while a life cycle assessment approach is used to model social indicators. The capability of the model is validated by proposing a case study of Iran and the augmented ε-constraint method is employed to reach a trade-off between three pillars of sustainability while managerial insights are provided by employing a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for the optimal solution selection. Model's results and sensitivity analysis on key parameters, specify the best locations for Jatropha Curcas L. (JCL) cultivation and facility construction as well as an optimal value of supply chain variables, which finally proves that Iran has a high potential for biodiesel production from JCL that leads improving balanced socio-economic development in this country.

MEASURING LOW CARBON ENERGY, ECONOMIC AND ENVIRONMENTAL SUSTAINABILITY PERFORMANCE OF BRICS

Formulating a robust low-carbon strategy has now been critical to achieving sustainable economic development amid broad carbon emissions across the BRIC region thanks to rapid industrialization and international trade growth. Developing such a strategy requires measuring actual sustainability performance from the energy, economy and ecological perspectives in this region. Therefore, this study explores the relationship among BRIC countries’ energy, economic and environmental sustainability performance by applying a common weight data envelopment analysis (DEA)-like composite indicator approach. The indicators encompass a comprehensive, latest, appropriate and applicable set of indicators. The results reveal that China and Brazil outperform against other underlined countries of the region in achieving sustainability with an overall index score of 0.96. India stands in ranking on second with a steady and unchanged score in the region, followed by South Africa and Russia, standing in ranking on fourth and fifth, respectively, in this study. Inclusive scores of both countries indicate the actual and robust possible potential for better sustainability performances in the region. This study proposes a policy by lifting the cross-border trade for renewable energy to achieve long-term environmental sustainability in the BRICS region.

Measuring environmental sustainability performance of South Asia

Abstract Sustainability and low-carbon economy strategies have been widely studied in the past; nevertheless, researchers often ignore the effects of combining energy, economic, and environmental factors when assessing sustainability performance. To fill this gap, we measure the environmental sustainability performance of South Asia using a common weight Data Envelopment Analysis (DEA)-like mathematical model encompassing a comprehensive, up-to-date, and relevant set of indicators. In this paper, we present a measurement of South Asia’s environmental sustainability performance for the years 2001–2015. Our results reveal that Bhutan outperforms the rest of the countries in South Asia, with an overall index score of 0.72. Nepal comes second in the region, with a stable score, followed by the Maldives. Afghanistan and Bangladesh are ranked fourth and fifth, respectively, and their overall scores indicate strong potential sustainability performances. India, Sri Lanka and Pakistan follow this list, with poor scores, with Pakistan having the lowest scores during the study period. Based on these findings, we propose a policy to boost the cross-border renewable energy trade for long-term environmental sustainability.

A common-weight DEA model for multi-criteria ABC inventory classification with quantitative and qualitative criteria

ABC analysis is a famous technique for inventory classification. However, this technique on the inventory classification only considering one indicator even though other important factors may affect the classification. To address this issue, researchers have proposed multiple criteria inventory classification (MCIC) solutions based on data envelopment analysis (DEA)-like methods. However, previous models almost evaluate items by different weight sets, and the index system only contains quantitative criteria and output indicators. To avoid these shortcomings, we propose an improved common-weight DEA model for MCIC issue. This model simultaneously considers quantitative and qualitative criteria as well as establishes a comprehensive index system that includes inputs and outputs. Apart from its improved discriminating power and lack of subjectivity, this non-parametric and linear programming model provides the performance scores of all items through a single computation. A case study is performed to validate and compare the performance of this new model with that of traditional ABC analysis, DEA–CCR and DEA–CI. The results show that apart from the highly improved discriminating power and significant reduction in computational burden, the proposed model has achieved a more comprehensive ABC inventory classification than the traditional models.

Incorporating decision makers’ preferences into DEA and common weight DEA models based on the best–worst method (BWM)

Incorporating decision makers’ (DMs’) judgments and preferences into DEA models is very important in some real-world problems. This paper presents an integrated data envelopment analysis (DEA)—best–worst method (BWM)—for considering DMs’ preferences in DEA and reducing flexibility in weights of inputs and outputs. First, the preferences vectors are designed using BWM, and then, a multi-objective DEA-BWM model is introduced. The proposed DEA-BWM model simultaneously maximizes the efficiency scores of DMUs and considers DMs’ preferences about weights of inputs and outputs. Finally, a goal programming model is suggested for extending the DEA-BWM model and finding common weights of inputs and outputs based on the DMs’ judgments. The proposed common weight DEA-BWM (CWDEA-BWM) model maximizes the efficiencies of DMUs, considers DMs’ preferences and uses a set of common weights. In order to illustrate the capability of proposed models, a numerical example is solved. Moreover, the proposed DEA-BWM and common weight DEA-BWM models are applied to evaluate 39 Iranian electricity distribution companies, and the results are analyzed and compared. The results indicate that the proposed DEA-BWM and CWDEA-BWM models are suitable for incorporating DMs’ preferences into DEA and fully ranking of DMUs.

Measuring performance with common weights: network DEA

In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model not only is linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide the top management team of a production system with an integrated framework to shape a better strategic decision process about firm performance, which is to treat the sources of inefficiencies and ultimately take corrective actions over the long run. Put differently, the proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm’s efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages of the organisation. In the context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model.

A 2-tuple integrated DEA-based approach for neuromarketing technology evaluation

Purpose Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area. Design/methodology/approach The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information. Findings The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants. Originality/value This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.

Comparative Analysis of the Development of the Digital Economy in Russia and EU Measured with DEA and Using Dimensions of DESI

The aim of this paper is to compare the development of the digital economy in Russia with that of the 28 countries of European Union (EU). Data were compiled from the European Commission’s International Digital Economy and Society Index (I-DESI 2018) database. After providing a brief overview of various alternative ways to measure the impact of information and communications technologies (ICT), we examine the most important features, advantages, and drawbacks of this database. We then describe the structure of our dataset and proceed with the analysis of the digital competitiveness of Russia and the EU-28. Our main research questions are concerned with the robustness of the EU data supply and the stability of its ranking. For this, we use the data envelopment analysis (DEA) method and the one-dimensional version of multidimensional scaling, which can also be employed for ranking issues. In addition to the conventional DEA method, we also investigate the viability of common-weights DEA models. We compare the results obtained to answer our questions. In evaluating the results, we also discern how data from Russia matches EU data on the digital economy. The comparison suggests that methods used in our study provide a similar solution, but the ranking of a few countries (including Russia) show wider variation.

Moringa oleifera biomass-to-biodiesel supply chain design: An opportunity to combat desertification in Iran

Moringa oleifera (M. oleifera) is a fast-growing, drought tolerant tree which has recently been identified as one of the prospective sources of biodiesel and one of the plants which can play a prominent role in the fight against desertification. In order to accelerate the commercialization of M. oleifera-based biodiesel, this paper develops a two-stage optimization model for the design of an efficient M. oleifera biomass to biodiesel supply chain. In the first stage, a common weight data envelopment analysis (CWDEA) model is utilized to rank locations threatened by desertification and land degradation. The locations with high vulnerability are introduced as candidate locations into the second stage in which a mixed-integer linear programming model is proposed to systematically design and optimize the entire supply chain and determine the optimal strategic and tactical supply chain decisions. To illustrate the application of the developed model, a case study in semi-arid lands of Iran is conducted. The results indicate that M. oleifera biodiesel production is not competitive with petroleum-based fuels but there is considerable room for cost reduction through improvement in M. oleifera productivity and oil conversion rate. Moreover, the areas at risk of desertification can be reclaimed by an increase of only 5% in the total supply chain cost.

Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran

Balanced development of regions requires the fair distribution of facilities and services. Hence, it is necessary to find and estimate the development degree of regions for policy makers. This paper presents an integrated Common Weight Data Envelopment Analysis (CWDEA) – Principal Component Analysis (PCA) model to find out the development degree of provinces in Iran. First, 131 suitable indicators are selected and then, the indicators are classified in fourteen different classes. In classical DEA model, each Decision Making Unit (DMU) is free to set its weights to reach the efficient frontier. In this paper, to restrict flexibility in indicator weights, development degree of provinces in each class is calculated using CWDEA model. Since, the proposed CWDEA model is not capable of fully ranking of provinces with all indicators, hence, the development degrees generated by CWDEA model are considered as indicators of PCA and the final ranks are obtained using PCA model. The results of proposed CWDEA-PCA model show that Yazd, Semnan and Bushehr are top three provinces in Iran.

Selection of Six Sigma project with interval data: common weight DEA model

PurposeThis paper aims to prioritize the most efficient Six Sigma project that can generate the greatest benefit to the organization, according to the relative performance among a set of homogenous projects (in here, DMUs). The selection of a Six Sigma project is a multiple-criteria decision-making problem, which is difficult in practice because the projects are not yet complete and the values of evaluation indicators are often interval or imprecise data. Managers stress the need for developing an effective performance evaluation methodology for selecting a Six Sigma project.Design/methodology/approachThis study proposes a modified model considering interval or imprecise data based on common weight data envelopment analysis (DEA) approach to solve problems on project selection.FindingsBy comparing its findings with an example from a previous study, the new model obtained realistic and fair evaluation results and significantly reduced the difficulties and the time spent during calculation. Moreover, not only the best project is identified, but also the exact indicator information is obtained.Originality/valueThis study solves the problem of selecting the most efficient Six Sigma project in the preference of interval or imprecise data. Many studies have shown how a Six Sigma project is chosen, but only a few have integrated interval data into the selection process.

An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach

Given the perennial imbalance and chronic scarcity between the demand for and supply of available organs, organ allocation is one of the most critical decisions in the management of organ transplantation networks. Organ allocation systems undergo rapid revisions for the sake of improved outcomes in terms of both equity and medical efficiency. This paper presents a Data Envelopment Analysis (DEA)-based model to evaluate the efficiency of possible patient-organ pairs for kidney allocation in order to enhance the fitness of organ allocation under inherent uncertainty in such problem. Eligible patient-kidney pairs are regarded as decision making units (DMUs) in a Credibility-based Fuzzy Common Weights DEA (CFCWDEA) approach and are ranked based on efficiency scores. Using a common set of weights for all DMUs ensures a high degree of fairness in the assessment and ranking of DMUs. The proposed model is also the first allocation method capable of coping with the vague and intervallic medical and nonmedical allocation factors by the aid of fuzzy programming. Verification and validation of the proposed approach are performed in two steps using a real case study from the Iranian kidney allocation system. First, the superiority of the proposed deterministic model in enhancing allocation outcomes is demonstrated and analyzed. Second, the applicability of the proposed fuzzy DEA method is demonstrated using a series of data realizations for different credibility levels.

A multiobjective DEA approach to ranking alternatives

The application of Data Envelopment Analysis (DEA) as a tool for efficiency evaluation has become widespread in public and private sector organizations. Since decision makers are often interested in a complete ranking of the evaluated units according to their performance, procedures that effectively discriminate the units are of key importance for designing intelligent decision support systems to measure and evaluate different alternatives for a better allocation of resources. This paper proposes a new method for ranking alternatives that uses common-weight DEA under a multiobjective optimization approach. The concept of distance to an ideal is thereby used as a means of selecting a set of weights that puts all the decision units in a favorable position in a simultaneous sense. Some numerical examples and a thorough computational experiment show that the approach followed here provides sound results for ranking alternatives and outperforms other known methods in discriminating the alternatives, therefore encouraging its use as a valuable decision tool for managers and policy makers.

Assessing sustainability and improvements in US Midwestern soybean production systems using a PCA–DEA approach

AbstractDocumentation of on-farm sustainability in agricultural sectors is becoming an essential element to ensure market access. An assessment process was developed to help soybean farmers document practices and verifiable advances in community, environmental and economic sustainability. Technical difficulties in analyzing and summarizing such assessment data include a large number of practices, correlation in variables, and use of discrete measures. By combining non-negative principal components analysis and common-weight data envelopment analysis, we overcame these difficulties to calculate a composite sustainability index for each individual farm and for the farm group as a whole. Applying this method to assessment data from 410 US Midwestern soybean farmers gave average sustainability scores of 0.846 and 0.842 for the soybean-specific and whole-farm assessments, respectively. Scenario analysis examined the impact if the bottom 10% of growers adopted the top ten sustainability drivers identified by the analysis. The average sustainability score only increased by 2%, but the minimum score increased from 0.515 to 0.647 for the soybean-specific assessment, and from 0.624 to 0.685 for the whole-farm assessment, while the lowest 10th percentile increased from 0.635 to 0.819 for the soybean-specific assessment, and from 0.634 to 0.920 for the whole-farm assessment. These results suggest that significant advancements could be made through focused efforts to improve adoption of sustainable practices by soybean farmers at the lower end of the spectrum.

A Common Weight Data Envelopment Analysis Approach for Material Selection

Material selection is one of the major problems in manufacturing environments since the improper selected material may lead to fail in the production processes and result in customer dissatisfaction and cost inefficiency. Every material has different properties which should be considered as major criteria during the material selection procedure. Selection criteria could be quantitative or qualitative. Quantifying the performance measures of qualitative criteria is an inevitable issue in the multi criteria decision making (MCDM) problems. In this paper, the common weight data envelopment analysis (CWDEA) model proposed by Hatefi et al. [27] is applied for material selection problem which accounts for both quantitative and qualitative criteria in an effective manner. However, through a numerical example borrowed from the literature, it is shown that the proposed method by Hatefi et al. [27] is not able to produce a full ranking vector our problem. Accordingly, the problem is resolved under different normalization methods and the resulting ranking vectors are then aggregated by the linear assignment method.

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon’s entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon’s entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology.