Fuzzy Data Envelopment Analysis Research Articles

Enhancing group decision-making: Maximum consensus aggregation for fuzzy cross-efficiency under hesitant fuzzy linguistic information

Group decision-making (GDM) is essential as it recognizes the inherent complexity of many decision scenarios, which frequently require the collective wisdom and knowledge of multiple decision-makers (DMs) to be effectively resolved. The proposed method aims to develop fuzzy data envelopment analysis (DEA) cross-efficiency models tailored to address GDM challenges, wherein attribute values are provided by DMs using hesitant fuzzy linguistic term sets (HFLTSs). For this purpose, we initially transform HFLTSs into their corresponding fuzzy envelopes, defined as trapezoidal fuzzy numbers (TrFNs). This conversion strategy effectively minimizes the loss in assessments based on HFLTSs while retaining the inherent ambiguity of the original information. Building upon this foundation, we develop fuzzy cross-efficiency models by leveraging the α-level sets of fuzzy envelopes. These models are designed to handle fuzzy input and output variables under various α-level sets, which are capable of considering all possible attribute values for each alternative. Following this, we implement a maximum consensus model using fuzzy cross-efficiency to assign weights to DMs. These weights facilitate the aggregation of individual fuzzy cross-efficiency intervals obtained from DMs’ assessments into collective ones, which serve to rank alternatives. Finally, we showcase the effectiveness and superiority of our proposal through numerical validation and comparative analysis.

Machine Learning for Optimizing Macro-ergonomics in Pharmaceutical Supply Chain

This study endeavors to enhance the macro-ergonomics of pharmaceutical supply chains by introducing an innovative hybrid AI methodology, incorporating fuzzy data envelopment analysis (FDEA). A comprehensive case study is conducted to evaluate the efficiency of the pharmaceutical supply chain, focusing on macro-ergonomic work system assessment. This evaluation aims to identify design flaws contributing to communication delays between physicians and patients. The proposed integrated approach utilizes a hybrid AI framework, specifically FDEA, to accurately measure macro-ergonomic influences on the healthcare supply chain under uncertain conditions. The case study involves a prominent urban outpatient medical facility with 20 clinics, selecting 20 Decision-Making Units for a holistic perspective. Results uncover factors causing delays, emphasizing weak or absent feedback structures as critical elements affecting the healthcare supply chain’s effectiveness. In conclusion, the study recommends modifications to optimize the pharmaceutical supply chain, enhancing overall healthcare efficiency. The findings provide valuable insights for healthcare management, underscoring the crucial role of advanced AI methodologies in addressing complex challenges within healthcare supply chains.

A Malmquist fuzzy data envelopment analysis model for performance evaluation of rural healthcare systems

The primary purpose of this article is to measure the relative efficiency and productivity change over time in rural healthcare systems in the presence of fuzzy data. First, a novel ranking function based on the lower and upper bounds of alpha-cut of the trapezoidal fuzzy numbers (TrFNs) is proposed to compare the TrFNs. The suggested ranking technique is used to construct the fuzzy data envelopment analysis (FDEA), Malmquist fuzzy DEA (Mal-FDEA), and undesirable Malmquist fuzzy DEA (UN-Mal-FDEA ) models. The proposed models evaluate the efficiency and productivity of decision-making units (DMUs) when the input and output data are given in the form of TrFNs. In addition, a case study of the rural healthcare system in a developing country has been considered to demonstrate the applicability of the developed models. The work considers number of sub-centers (SCs), the number of primary health centers (PHCs), the number of community health centers (CHCs), nursing Staff at PHCs, an auxiliary nurse and midwives (ANM) at SCs, doctors at PHCs, pharmacists at PHCs, laboratory technicians at PHCs, radiographers at CHCs, and specialists at CHCs as input parameters and average population covered by CHCs, average village covered by CHCs, number of patients, and infant mortality rates as output parameters to analyze the performance of the rural healthcare systems. We show the UN-Mal-FDEA model has a higher production value than the Mal-FDEA model. The results of our proposed models enable us to recognize inefficiencies that states may rectify without compromising healthcare quality.

Resilience-sustainability assessment of electricity supply chain under uncertainty: a new fuzzy dynamic network DEA based on rang adjusted measure

Resilience-sustainability assessment of electricity supply chain under uncertainty: a new fuzzy dynamic network DEA based on rang adjusted measure

Precision in decision-making: a novel Z-number DEA approach for European country rankings

PurposeConventional approaches such as Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) cannot effectively account for uncertainty, which can lead to imprecise decision-making. Furthermore, these methods frequently rely on precise numbers, ignoring the inherent uncertainty of real-world data. To address this gap, the research question arises: How can we develop a methodology that combines Z-number theory and FDEA to provide a comprehensive assessment of residency preferences in European countries while accounting for uncertainty in information reliability? The proposed methodology aims to fill this gap by incorporating Z-number theory and FDEA.Design/methodology/approachThe proposed study assesses residency preferences across 39 European countries, focusing on key factors like environment, sustainability, technology, education, and development, which significantly influence individuals' residency choices. Unlike conventional DEA and FDEA approaches, the proposed method introduces a novel consideration: dependability. This inclusion aims to refine decision-making precision by accounting for uncertainties related to data reliability. The proposed methodology utilizes an interval approach, specifically employing the a-cut approach with interval values in the second step. Unlike using crisp values, this interval programming resolves formulations to determine the efficiencies of decision-making units (DMUs).FindingsThe comprehensive findings provide valuable insights into the distinctive factors of European nations, aiding informed decision-making for residency choices. Malta (75.6%-76.1%-75.8%), Austria (78.2%-78%-76.1%), and the United Kingdom (79.3%-78.4%-77%) stand out with distinct characteristics at levels of a = 0-a = 0.5-a = 1, assuming the independence of variables of the overall evaluation. Individual consideration of each factor reveals various countries as prominent contenders, except for the environmental factor, which remains consistent across countries.Originality/valueTraditional DEA models encounter challenges when dealing with uncertainties and inaccuracies, particularly in the evaluation of large systems. To overcome these limitations, we propose integrating Z-numbers—a powerful mathematical tool for modeling uncertainty—into the conventional DEA process. Our methodology not only assesses the effectiveness of countries across various socio-economic and environmental metrics but also explicitly addresses the inherent uncertainties associated with the data. By doing so, it aims to enhance the precision of decision-making and provide valuable insights for policymakers and stakeholders.

New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores.

Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.

A new intuitionistic fuzzy scheme of data envelopment analysis for evaluating rural comprehensive health service centers

In this study, the classical CCR-IO DEA model (an input-oriented form of the DEA model of Charnes, Cooper, and Rhodes [24] is extended to an intuitionistic fuzzy environment. For this aim, the trapezoidal intuitionistic fuzzy numbers are used to show the values of inputs and outputs. An intuitionistic fuzzy number is characterized by membership, non-membership, and hesitancy degrees. Therefore, such type of fuzzy numbers is more complete and contains more information to reflect a fuzzy type of uncertainty comparing to the conventional fuzzy values. In order to solve the proposed intuitionistic fuzzy CCR-IO model, for the first time a possibility-based approach is developed for the intuitionistic fuzzy DEA models. In this approach for the first time the ME measure of fuzzy events is extended to intuitionistic fuzzy DEA models, therefore, some new definitions and propositions are presented and clarified. This measure is more flexible and effective comparing to the other measures such as credibility, necessity, and possibility measures. A case study from the health care sector of Iran is considered to evaluate the proposed solution approach numerically. The case study is solved under different scenarios, a full sensitivity analysis is performed, and finally some useful managerial insights are presented. In addition, the results of the proposed intuitionistic CCR-IO model are compared to the classical models of the literature.

Cost efficiency analysis of public sector general insurers by fuzzy DEA approach

Cost efficiency analysis of public sector general insurers by fuzzy DEA approach

Optimizing Failure Modes and Effects Analysis with Fuzzy Multiattribute Grey Theory and DEA

Optimizing Failure Modes and Effects Analysis with Fuzzy Multiattribute Grey Theory and DEA

A Novel Pythagorean Approach Based Sine-Shaped Fuzzy Data Envelopment Analysis Model: An Assessment of Indian Public Sector Banks

A Novel Pythagorean Approach Based Sine-Shaped Fuzzy Data Envelopment Analysis Model: An Assessment of Indian Public Sector Banks

Financial efficiency of healthcare companies in an emerging market

AbstractUnderstanding the financial dynamics of healthcare companies is essential for reducing costs while improving access to care and patient outcomes. This study aims to explore the financial efficiency of healthcare companies operating in an emerging stock market. We use conventional and fuzzy data envelopment analysis. We consider liquidity and leverage ratios as input criteria and profitability ratios as output criteria, each taken from Borsa Istanbul for the year 2022. The results indicate that financially inefficient healthcare companies can increase their efficiency by effectively managing debt and working capital, restructuring their asset base, and focusing on equity management.

Employing fuzzy DEA for Green-resilient supplier selection in an electronic industry of household appliances: a case study (Snowa)

Employing fuzzy DEA for Green-resilient supplier selection in an electronic industry of household appliances: a case study (Snowa)

Fuzzy DEA Efficiency Analysis with the Acceptance Degree of the Violated Fuzzy Constraints

Fuzzy DEA Efficiency Analysis with the Acceptance Degree of the Violated Fuzzy Constraints

Hierarchical fuzzy DEA model with double frontiers combined with TOPSIS technique: application on mobile money agents locations

Hierarchical fuzzy DEA model with double frontiers combined with TOPSIS technique: application on mobile money agents locations

Measuring the eco-efficiency of municipal solid waste service: A fuzzy DEA model for handling missing data

In the last decades, evaluating the performance of municipal solid waste (MSW) service has attracted the interest of scholars and policymakers worldwide. This study adopts a generalized directional distance function (GDDF) data envelopment analysis (DEA) model to measure the eco-efficiency of 94 Apulian municipalities providing MSW service over 2019–2021. The model uses the α-cut method to handle missing data in a fuzzy DEA environment. During the period covered by the research, on average, the MSW eco-efficiency was between 0.825 and 0.879. The MSW separate collection rate is the only factor significantly affecting the MSW eco-efficiency.

Measuring power consumption efficiency of an electromechanical system within a long-term period by fuzzy DEA and TOPSIS for sustainability

Measuring power consumption efficiency of an electromechanical system within a long-term period by fuzzy DEA and TOPSIS for sustainability

A Novel Game Modeling of Multi-Criteria Decision-Making Based on Generalized Fuzzy Data Envelopment Analysis: An Empirical Study of Traditional Chinese Medicine Hospitals

A Novel Game Modeling of Multi-Criteria Decision-Making Based on Generalized Fuzzy Data Envelopment Analysis: An Empirical Study of Traditional Chinese Medicine Hospitals

Evaluating the efficiency of the Indian airports using fuzzy DEA method

Evaluating the efficiency of the Indian airports using fuzzy DEA method

External R&D Supplier Evaluation and Selection: A Three-Stage Integrated Funnel Model

Rapidly changing market demand has seen more and more companies pay attention to external R&D, to make full use of external resources, improve competitiveness, and promote innovation. The suitability of external R&D suppliers directly impacts the success of R&D. However, existing research in this topic has limitations. First, few existing studies have examined the evaluation and selection of external R&D suppliers, quantitatively. Second, there are few studies on the evaluation of effectiveness of R&D suppliers from the perspective of customer demand. Third, the evaluation of prototypes involves heterogeneous data for different technical criteria. As yet, there is no specific suitable model. To bridge abovementioned gaps, this article proposes a comprehensive three-stage integrated funnel model for external R&D supplier evaluation and selection. Specifically, it evaluates the effectiveness of suppliers from the perspective of customer demand and improves the nonradial superefficiency Pythagorean fuzzy DEA methodology. In addition, it proposes a novel fuzzy MA-VIKOR submodel based on heterogeneous data, which can evaluate suppliers from two perspectives: group utility and individual regret, while avoiding the potential problem of equal ranking. Finally, the effectiveness of the proposed model is illustrated by an external R&D supplier selection case of Company J, an international communication equipment company.

Assessing the Relative Impact of Colombian Higher Education Institutions Using Fuzzy Data Envelopment Analysis (Fuzzy-DEA) in State Evaluations

This research aims to design a helpful methodology for estimating universities' relative impact on students as a sustainability factor in higher education. To this end, the research methodology implemented a two-stage approach. The first stage involves the relative efficiency analysis of the study units using Fuzzy Data Envelopment Analysis. The second stage consists of a predictive evaluation of the efficiency of the study units. Consequently, among the most relevant results of the research, it is observed that the methodology identifies the institutions that need to strengthen the academic competencies of the industrial engineering program. Additionally, we developed a benchmark analysis called Efficient Route to help inefficient units achieve efficiency, associating efficiency, and sustainability as pillars of higher education processes.