Fuzzy Data Envelopment Analysis Model Research Articles

Assessing the stability of suppliers using a multi-objective fuzzy voting data envelopment analysis model

Assessing the stability of suppliers using a multi-objective fuzzy voting data envelopment analysis model

A novel fuzzy network DEA model to evaluate efficiency of Iran’s electricity distribution network with sustainability considerations

Iran’s electricity distribution network (EDN) includes three stages of generation, transmission, and distribution, with different types of input and output criteria and complex communications with both parallel and serial structures. This study formulates a Fuzzy Network DEA (FNDEA) model for assessing the efficiency of Iran’s EDNs components with sustainability considerations and uncertain data. In order to utilize all input and output criteria, this study also proposes a fuzzy linear programming model to determine the optimal lower bound for all input and output weights. Furthermore, appropriate policies are suggested based on the strengths and weaknesses of each EDN to improve its efficiency. Finally, a sensitivity analysis has been implemented to validate the proposed model. The results of the present study reveal that the transmission stage performed better than the generation and distribution stages. It also shows that the distribution stage has the greatest impact on the overall performance of Iran's EDN.

Detection of the Type of Left and Right Returns-to-Scales Using Envelopment DEA Models in Crisp and Fuzzy Environments: An Application for Predicting Changes in the Stock Market

This paper aims to analyze left and right returns-to-scales in crisp and fuzzy data envelopment analysis (DEA). Since all previous envelopment DEA models for assessing left and right returns-to-scales are parametric, they are prone to encountering infeasibility problems, producing incorrect or different solutions for determining the type of returns-to-scale because of the different choices of parameter values. This misdiagnosis will lead to poor management decisions. Due to the mentioned problems, the issue of one-sided returns-to-scale has also not been studied in inaccurate environments. The present paper first proposes an alternative method of left and right returns-to-scales determination with crisp data to address this problem. This approach develops two non-parametric envelopment DEA models for analyzing left and right returns-to-scales. Then, the proposed method is extended to the fuzzy environment where data are considered more realistic. Due to its major advantages, credibility measure is used for solving fuzzy DEA models built to determine left and right returns-to-scales. As an application of the proposed method, data of companies in the Iran stock market are collected for 2014–2019 as fuzzy data and frontier units are analyzed by one-sided returns-to-scale.

Bridging the knowledge gap between technology and business: An innovation strategy perspective.

Decision-makers (DMs) are not sufficiently exposed to concepts such as efficiency and risk in innovative activities from the perspective of organizational strategy. The challenges become even greater when these DMs lack expertise in technology and deal with uncertain circumstances. In this sense, exchanging expert knowledge between DMs and technical teams will strengthen the link between technology planning and strategic management. The purpose of this study is to bridge the knowledge gap between these two groups. It introduces a framework to translate the organization’s strategy into technological decisions at an acceptable innovation risk level. This framework considers aspects such as knowledge, type of innovation, and innovation process. This study focuses on determining whether activities should be accepted or rejected by examining the uncertainty and efficiency of innovation. It also introduces a novel perspective on the hybrid "success-failure" uncertainty of innovation, and a new measure called "efficiency probability," which DMs and technology developers can use to intuitively engage in the innovation process. This paper seeks to propose a practical strategy map for new product development under uncertain conditions. To achieve this goal, the Fuzzy Front-End (FFE) concept, fuzzy data envelopment analysis (FDEA) model, and adjustable possibilistic programming (APP) approach are applied. The results of this study indicate that innovative activities typically have low efficiency and high uncertainty. Therefore, the decision to implement or abandon them requires reviewing and balancing the goals and strategic approach of the organization with technological and business features.

An Integrated Data Mining Framework for Organizational Resilience Assessment and Quality Management Optimization in Trauma Centers

Every second counts for patients with life-threatening injuries, and trauma centers deliver timely emergency care to patients with traumatic injuries. Quality assessment and improvement are some of the most fundamental concerns in trauma centers. In this study, a comprehensive organizational resilience approach is proposed to evaluate performance in trauma centers using the European Foundation for Quality Management as a fundamental and strategic approach. We propose a unique intelligent algorithm composed of parametric and non-parametric statistical methods to determine the type and the extent of influence within the organizational resilience and quality management perspectives. We use structural equation modeling to examine the reliability and validity of the input data. The efficiency of each trauma center is then measured using a machine learning method with genetic programming, support vector regression, and Gaussian process regression. The mean absolute percentage error is used to determine the optimal model, and a fuzzy data envelopment analysis model is used to verify and validate the results obtained from the optimal model. The results show that customer results, human capital results, and key performance results have the highest importance weights and positive influence on quality management. Cognitive resources, roles and responsibilities, and self-organization have the highest importance weights and positive influence on organizational resilience.

An analytical approach for evaluating the impact of blockchain technology on sustainable supply chain performance

Nowadays, distributed ledger technologies (e.g., blockchain technology) can play significant roles in improving supply chain management and sustainability. Blockchain technology can support responsible sourcing and ensure compliance with environmental standards by boosting traceability and transparency in sustainable supply chains. Nevertheless, blockchain technology has not been widely applied in this field due to the lack of familiarity of managers with its intrinsic characteristics. This study investigates the performance improvement, arising from the blockchain implementation, to address the problem of managerial conservatism and this technology adoption. Accordingly, an analytical approach is proposed to identify blockchain technology adoption enablers and analyze their impact on supply chain performance. At first, the main enablers, derived from the literature review, are explored using network theory. The existing causal relationships between enablers are extracted based on a multi-expertise team members’ agreement. Then, the fuzzy inference system is employed to determine the weights of relationships between the identified enablers and supply chain performance-related targets. After modeling the extracted causal relationships using the fuzzy cognitive map model, a scenario is defined for each enabler, and its impact on improving supply chain performance is estimated by implementing the hybrid learning algorithm. Finally, the obtained outputs are used to prioritize the blockchain technology adoption enablers using the fuzzy data envelopment analysis model. The result of this study implies that the blockchain technology can have a significant impact on mineral supply chain performance by creating smart contracts and enhancing environmental sustainability, traceability, and transparency.

Evaluation of Online Markets Considering Trust and Resilience: A Framework for Predicting Customer Behavior in E-Commerce

The evolution of electronic commerce has led to expanding online markets due to convenience, online incentives, greater access to information, broader selections, competition, pricing, product quality, and lead time, i.e., the time that it takes to deliver the products after an order is placed. However, online markets have captured less attention in environments where consumers are concerned about the security and privacy of their personal data. Such concerns can be due to several reasons, from the reluctance in using electronic commerce to the lack of trust and reliability of web vendors. In this paper, we study trust and resilience engineering in such online markets and identify the most and least significant factors impacting online shopping systems. To tackle the inherent uncertainties of data used in our analysis, we propose a Fuzzy Data Envelopment Analysis (FDEA) model and validate our findings. Numerical examples show the efficiency of the proposed method in evaluating online and offline shopping systems with respect to trust and resilience.

Performance Evaluation of Public Management in View of Fuzzy DEA Model

The research on management performance evaluation and governance countermeasures of public utilities, as an important entry point, focus, and flash point of the combination of management science, engineering research, and public management research, not only highlights the implementation and implementation of the scientific outlook on development and correct outlook on political performance, so that the topic has distinct theoretical frontier, practical pertinence, and practical guidance. It also highlights the intersection and integration of multiple disciplines, combines normative research with empirical research, qualitative research with quantitative research, macro research with micro research, and method and technology research with concept innovation and institutional arrangement research, and strives to realize the integration and complementarity of research methods of management science and engineering and public management. The research of public management broadens new horizons, expands new fields, and provides new paradigms, methods, and means. Based on the weight of public utilities management performance evaluation indicators and the selection of empirical samples, this paper studies the basic principles and comparative advantages of fuzzy comprehensive evaluation model and DEA model, makes an empirical evaluation of public utilities management performance, draws evaluation conclusions, and analyzes the key factors affecting and restricting the level of public utilities management performance. From the research level of evaluation technology and methods to the level of institutional arrangement, it is the only way to realize the scientific, standardized, and institutionalized performance evaluation of local government public utilities management. The basic procedures of performance evaluation and the performance evaluation mechanism are introduced, and the establishment and improvement of the information fidelity system and result application system of public utilities performance evaluation are studied. Based on the evaluation conclusion and empirical analysis of public utilities management performance, aiming at the key factors affecting the level of public utilities management performance, this paper puts forward the governance countermeasures of public utilities from the aspects of establishing scientific public utilities management performance concept, innovating public utilities management system, establishing and perfecting public utilities investment management mechanism, and so on.

Analysis of Public Management Performance Evaluation Model Based on Fuzzy DEA Model

The evaluation of public management performance is the key to identify the effectiveness of the work of public administration, and public policy is an important guarantee for the orderly promotion of the work of public administration; therefore, it is important to analyze the effectiveness of public management policies to evaluate public management performance. Taking environmental protection policies as an example, this paper constructs an environmental‐economic DEA efficiency analysis model using the DEA method to conduct a comprehensive evaluation of EEE in different provinces of China. It is found that EEE is highly correlated with the regional economic development level and is highly influenced by policy fluctuations, but the overall EEE of different provinces from 2016 to 2020 tends to be consistent and has a certain degree of stability. The conclusions of this paper have important implications for policy guidance, emphasizing the need to focus on the coordination of economic development and environmental protection, and the need for the improvement of the environmental policy system to be continuously optimized in an incremental manner, taking into account the actual situation of local development.

Selecting a green supplier utilizing the new fuzzy voting model and the fuzzy combinative distance-based assessment method

• In this paper, the selection of a Green Supplier is performed using a new hybrid Multi Attribute Decision Making-Data Envelopment Analysis method. • The fuzzy version of the Preferential Voting model is designed in this paper. • A new secondary goal model using the Combinative Distance-based Assessment method is proposed in the Cross Efficiency ranking method. • The proposed new method for Selecting a Green Supplier in an Automotive Group has been implemented. Significant increase in environmental concerns has led organizations to consider the necessary measures to apply green supply chain management to improve their environmental and economic performance. An important way to implement green supply chain management is to revise the methods of purchasing and selecting green suppliers. In this paper, a new fuzzy Data Envelopment Analysis (DEA) model for green supplier selection by collecting expert votes is presented. Then a way to improve the cross-efficiency method to achieve a complete ranking is suggested. In the improved method, a new secondary goal model based on the concepts of the Fuzzy COmbinative Distance-based ASsessment (CODAS) method will be presented. The proposed models were then used to select a green supplier in an automotive group. In the present case study, a complete ranking for green suppliers is obtained.

Urban circular economy performance evaluation: A novel fully fuzzy data envelopment analysis with large datasets

This study constructs a fully fuzzy data envelopment analysis (DEA) with large datasets to evaluate the urban circular economy. The proposed fully fuzzy DEA model considers uncertainties of circular economy indicators and introduces fuzzy trigonometric numbers. Additionally, the model provides a modified algorithm to overcome calculation difficulties due to voluminous data and large-scale decision-making units. The proposed model can quickly solve urban circular economy efficiency under uncertainty and with large datasets. An empirical study of 264 Chinese cities over 2009–2018 was conducted. Overall, the average annual fuzzy efficiency scores (theoretically vary from 0˜ to 1˜) of the urban circular economy in these cities are (0.7471, 0.7463, 0.7451), indicating that there is substantial room for improvement. The average efficiency scores and the subitem coordination levels of western cities are the highest compared to those of the other regions. Moreover, Northeast China exhibits the lowest efficiency score, which may be attributed to its decaying industry and unadvanced technical level. The overall urban economy performance presents two distinct trends: in 2009–2015, the urban circular economy exhibited negative growth, whereas it increased in 2015–2018. However, the growth rate declined, and negative growth remains a risk. Based on the results, several policy implications are provided for promoting urban circular economy.

Evaluation of the Efficiency of European Health Systems Using Fuzzy Data Envelopment Analysis.

Many studies that assess efficiency in health systems are based on output mean values. That approach ignores the representativeness of the average statistic, which can become a serious problem in estimation. To solve this question, this research contributes in three different ways: the first aim is to evaluate the technical efficiency in the management of European health systems considering a set of DEA (Data Envelopment Analysis) and FDEA (Fuzzy Data Envelopment Analysis) models. A second goal is to assess the bias in the estimation of efficiency when applying the conventional DEA. The third objective is the evaluation of the statistical relationship between the bias in the efficiency estimation and the macroeconomic variables (income inequality and economic freedom). The main results show positive correlations between DEA and FDEA scores. Notwithstanding traditional DEA models overestimate efficiency scores. Furthermore, the size of the bias is positively related to income inequality and negative with economic freedom in the countries evaluated.

Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics

Artificial intelligence (AI) applications are the core challenge for engineering and management science concepts in production and logistics within the next decade. This study analyses the application of AI instances in route planning as a central part of logistics management from an empirical case perspective for retail logistics in Germany. The methods applied encompass fuzzy data envelopment analysis (DEA), slack-based measurement (SBM) fuzzy DEA, and analytic hierarchy process (AHP)-SBM Fuzzy DEA. For the two depots using AI-based routing to the full account, efficiency advantages can be shown in the Fuzzy DEA as well as the SBM fuzzy DEA models. Results further indicate that the methodological approach is adequate for the analysed problem and that the combination with AHP is an interesting addition as, e.g., the perspective of sales managers supersedes that of logistics managers for route planning efficiency – a thought-provoking result pointing at very customer-oriented logistics systems.

A novel fuzzy non-radial data envelopment analysis: An application in transportation

The slack-based measure (SBM) DEA model is a non-radial model used to calculate the relative efficiency, input, and output targets of the different decision-making units (DMUs) based on their best peers or efficient frontier. The conventional SBM DEA model used crisp inputs and outputs. But, it can be observed in real-life problems that sometimes the available data is in linguistic forms such as “few”, “many”, “small”, or missing data. The DEA technique is frontier based, and therefore, imprecise data may lead to untenable results. Fuzzy theory, which is already established to handle uncertain data, can overcome this problem. Furthermore, the sensitivity and stability analysis have been checked the robustness of fuzzy DEA models. In this study, sensitivity and stability analysis of the fuzzy SBM DEA has been performed. The lower and upper sensitive bounds for inputs and outputs variables have been obtained for both the inefficient and efficient DMUs to calculate the input and output targets. Finally, a real-life transportation problem for the validity of the study is presented for its depiction.

DeaR-Shiny: An Interactive Web App for Data Envelopment Analysis

In this paper, we describe an interactive web application (deaR-shiny) to measure efficiency and productivity using data envelopment analysis (DEA). deaR-shiny aims to fill the gap that currently exists in the availability of online DEA software offering practitioners and researchers free access to a very wide variety of DEA models (both conventional and fuzzy models). We illustrate how to use the web app by replicating the main results obtained by Carlucci, Cirà and Coccorese in 2018, who investigate the efficiency and economic sustainability of Italian regional airport by using two conventional DEA models, and the results given by Kao and Liu in their papers published in 2000 and 2003, who calculate the efficiency scores of university libraries in Taiwan by using a fuzzy DEA model because they treat missing data as fuzzy numbers.

Assessing the sustainable supply chains of tomato paste by fuzzy double frontier network DEA model

Assessing the sustainable supply chains of tomato paste by fuzzy double frontier network DEA model

A credibility approach on fuzzy slacks based measure (SBM) DEA model

Data Envelopment Analysis (DEA) is a multi-criteria technique based on linear programming to deal with many real-life problems, mostly in nonprofit organizations. The slacks-based measure (SBM) model is one of the DEA model used to assess the relative efficiencies of decision-making units (DMUs). The SBM DEA model directly used input slacks and output slacks to determine the relative efficiency of DMUs. In order to deal with qualitative or uncertain data, a fuzzy SBM DEA model is used to assess the performance of DMUs in this study. The credibility measure approach, transform the fuzzy SBM DEA model into a crisp linear programming model at different credibility levels is used. The results came from the fuzzy DEA model are more rational to the real-world situation than the conventional DEA model. In the end, the data of Indian oil refineries is collected, and the efficiency behavior of the companies obtained by applying the proposed model for its numerical illustration.

A Computational Procedure Using Robust Ranking Method to Formulate and Solve Fuzzy DEA Models

In some real-world applications, performance assessment often has to be considered under uncertainty with fuzzy input and output. Data envelopment analysis (DEA) is a new and effective approach to measuring the relative efficiency of decision-making units concerned with performance evaluation. Recently, DEA has studied with fuzzy input and output. In this paper, we present a computational procedure using a robust ranking method to formulate and solve fuzzy DEA models. Based on this approach, we convert the fuzzy DEA input and output from fuzzy form to crisp. Also, a case study has been presented as in [6] to assess the performance of eight manufacturing enterprises with fuzzy data.

Developing a new integrated artificial immune system and fuzzy non-discretionary DEA approach

As a useful performance evaluation and decision-making tool, data envelopment analysis (DEA) has been proven to be an excellent data-oriented efficiency analysis method when there are multiple inputs and outputs. However, when working with large datasets, DEA requires more time to solve and calculate the optimal values for each decision-making unit (DMU). To close this methodological gap, this study proposes a new integrated fuzzy nondiscretionary DEA (FNDEA) model and artificial immune system (AIS) to predict and find the optimal values of DMUs. In so doing, we first modify an FNDEA model to classify the set of all DMUs into efficient and inefficient, and immune system (AIS) is used to predict and find the optimal values of DMUs. Then, a modified fuzzy nondiscretionary additive DEA model, which is designed as a middle chain, is used for sensitivity analysis of inefficient DMUs to determine their target outputs, which are called antigens. Finally, we try to reduce the distance between these two steps in order to predict the optimal values (with nearest distance to antigens) of inefficient DMUs and improve their efficiency by using a combined AIS and FNDEA model called the FNDEA–AIS approach. To illustrate the advantages of the proposed FNDEA–AIS approach, a dataset from 24 Iranian forest management units is collected; the results indicated that our new FNDEA–AIS approach (in comparison with other well-established performance prediction techniques) exhibits better convergent validity and high correlation with low error rate to predict optimal values of inefficient DMUs. The main conclusion is that our new applied method, developed for the first time herein, provides a suitable improvement over previously developed methods, such as artificial neural networks (ANNs), to detect inefficiency and to improve the overall performance when analyzing different types of real-life problems.

A New Fuzzy DEA Model for Solving the MCDM Problems in Supplier Selection

Supplier selection is a crucial task in supply chain management, and good supply chain management is essential to a company's ability to develop sustainably and gain the trust and satisfaction of its customers. In modern supply chain management, supplier selection often takes the form of a multi-criteria decision-making (MCDM) problem. Thus, to solve this problem, this study formulated a novel fuzzy MCDM model, which is based on the fuzzy data envelopment analysis model. This novel approach can transform the linguistic variables of assessment criteria into well-founded quantitative data for calculation. Moreover, our approach can effectively discriminate between decision-making units (both efficient and inefficient) to aid managers in identifying the most suitable suppliers. We hope that our model can provide scholars and managers with new insights into solving the MCDM problem in supplier selection.