Fuzzy data envelopment analysis (FDEA) plays an essential role in the current socio-economic scenario to analyze the performance of decision-making units (DMUs) within a fuzzy environment. This paper introduced a novel Bipolar Fuzzy Data Envelopment Analysis (BFDEA) model using bipolar triangular fuzzy numbers to accommodate both uncertainty and ambiguity in evaluating the performance of a finite number of DMUs. The BFDEA model utilizes a value function for bipolar fuzzy numbers and translates BFDEA models into equivalent crisp models, thus providing thorough and precise evaluations of efficiency. The BFDEA model embraces a super-efficiency framework to offer a full ranking of efficient DMUs, while establishing a benchmarking framework for a meaningful discussion of improvements in performance. A numerical example showed that the BFDEA method could provide a reliable nuanced evaluation even in the presence of conflicting information. This work contributes to the DEA literature, where uncertainty has been inadequately addressed up till the present, by providing breakthroughs in a convincing way for decision makers to analyze performance amidst complicated and indeterminate situations.
Recently, the food industry has faced numerous challenges such as rising demand, climate change, and the imperative to improve the quality and safety of food products. This research investigated the role of Artificial intelligence (AI) and the Internet of Things (IoT) in managing food supply and distribution projects. The main objective of this study was to analyze how these technologies could be implemented to optimize the process of supply chain and enhance the efficiency and effectiveness in food distribution. Successful cases of technological implementation in the food industry highlighted the associated benefits and challenges of adopting AI and IoT. Ten critical factors influencing the roles of AI and IoT in food supply and distribution were identified and considered in the current study. Following a systematic coding process through meta-synthesis, concepts related to each factor were extracted from previous studies. Finally, expert opinions were gathered by a questionnaire survey whereas the Kappa index was calculated using SPSS software. The obtained value of 0.78 indicated a desirable agreement in the perspectives between researchers and experts. By leveraging AI, organizations are able to analyze big data, predict demand, optimize inventory, and reduce resource waste. Likewise, IoT, through connecting devices and sensors to the network, enables the collection of real-time data, which assists managers in making better decisions regarding the timing and location of food distribution.
Applying the Theory of Planned Behavior (TPB), this study provided an enhanced understanding of the intentions, motivations, and beliefs about blood donation among the young generation in the U.S. An online quantitative Qualtrics survey was administered at a large public university to collect data from the campus community, with participants aged 18 to 39 (N = 954). Data were collected via an adapted questionnaire on the TPB constructs: attitudes towards blood donation, subjective norms of peers and loved ones, perceived control of behavior, and intention to donate blood. Univariate, bivariate and multivariate analysis were employed to explore the associations of these constructs. Primary findings revealed that the intention to donate blood regularly was positively associated with social norms. Secondary findings suggested that a hierarchical multiple regression analysis provided strong support for the role of social media apps as a major determinant of motivations for donating blood, with TPB constructs accounting for 34% of the variance. Tertiary findings from this study derived Cronbach’s $\alpha$ = 0.555, indicating a poor level of internal consistency. The generalizability of the results in this study could be verified by increasing the number of questions in each construct and conducting future studies at larger universities and blood centers.
Managing the public sector increasingly requires the application of modern analytical methods that enable decision-making based on multiple criteria. This paper presents a real-world case study in which multicriteria decision-making (MCDM) method sare applied to evaluate the marketing activities and performance of a public institution. The research includes an analysis of the services offered, user satisfaction, and a comparison with alternative institutions in the same field. The obtained results highlight the relevance of MCDM methods for the objective assessment of public services and for strategic planning within the public sector. The paper contributes to a better understanding of the potential for applying MCDM tools in the context of public administration, with particular emphasis on marketing as a mechanism for improving transparency and effectiveness.
Imperfect production and rework in contemporary manufacturing systems, are inevitable realities hampering overall performance and cost efficiency. To address this challenge, this study developed an Economic Production Quantity (EPQ) model which integrated defective items, rework, disposal, and penalties for lost sales within a fuzzy decision-making framework. The convexity of the model implied the possible existence of an optimal solution. Compared to conventional crisp models, the proposed approach provided a more robust and realistic evaluation of inventory and cost structures by representing indeterminate parameters such as production cost, backordering cost, and penalty cost through Hexagonal Fuzzy Numbers (HFNs) and Graded Mean Deviation Method (GMDM) for defuzzification. The numerical illustration demonstrated superiority of the fuzzy model in minimizing the total cost, balancing inventory levels, and enhancing service quality. Sensitivity analysis further highlighted the adaptability of the model to combat unpredictable changes in the parameters. The study concluded with valuable insights for decision-makers to optimize imperfect production processes, strengthen resource allocation, and tackle uncertainty in real-world manufacturing environment.
The COVID-19 pandemic significantly challenged business resilience, particularly in the healthcare sector, where pharmaceutical and biotechnology companies experienced growth and service-oriented entities faced operational stress. In this study, the advanced Multi-Criteria Decision-Making (MCDM) techniques were employed to investigate the financial performance of healthcare firms listed on the Standard and Poor's 500 index from year 2018 to 2023. The research evaluated ten firms based on 16 criteria, encompassing both financial and non-financial dimensions. The financial criteria included Leverage Ratio, Tobin's Q Ratio, Revenue Growth, Operating Profit Growth, Equity Growth, Firm Size, Net Income, Total Liabilities, Revenue, Operating Profit, and Market Capitalization. In parallel, the non-financial indicators such as Human Resource Management, Supply Chain Management, Risk and Crisis Management, Business Ethics, and Environmental Policy were integrated to reflect managerial quality and sustainability practices. Out of the 16 criteria, two costs and nine benefits were quantitative whereas the remaining five benefits were qualitative. Expert assessments were modeled on the Spherical Cubic Fuzzy (SCF) sets and aggregated with the Aczel–Alsina operator. Alternatives were ranked using methods like the Ranking of Alternatives through Nested Cumulative Operator Method (RANCOM) and the Alternative Ranking Order Method with Adjustment Normalization (AROMAN), hence producing a multidimensional evaluation matrix enriched by both numerical and verbal judgments from ten experts. This research contributed to the literature in three key ways: (1) It provided a holistic assessment of financial performance in a highly dynamic and uncertain environment; (2) It broadened the performance evaluation framework to include non-financial and sustainability-driven criteria; and (3) It demonstrated the utility of novel MCDM tools like the SCF sets, the Aczel–Alsina aggregation, the RANCOM, and the AROMAN in complicated decision environments. The study offers a robust and innovative analytical model for academics and practitioners seeking to understand firm resilience and performance amid crises.
The accurate assessment of contract renewal risk at the individual policyholder level represents a critical component of risk management in non-life insurance and is essential for ensuring long-term business sustainability. In this study, a two-stage interval type-2 fuzzy decision-making framework was proposed to evaluate and classify policyholder renewal risk. The approach began with the identification of key risk factors (RFs) that exert the most significant influence on renewal outcomes and overall business risk. The relative importance of these RFs was expressed through predefined linguistic terms, which were systematically mapped to interval type-2 triangular fuzzy numbers (IT2TFNs). The Fuzzy Best-Worst Method (FBWM) was applied to derive the optimal weight vector of RFs. Subsequently, the values of the identified RFs were quantified based on available operational and historical insurance data. Using type-2 fuzzy algebra, a weighted normalized decision matrix was constructed. In the second stage, a novel Pareto analysis extended with interval type-2 fuzzy numbers (IT2FNs) was introduced to classify policyholders according to their associated renewal risk levels. This integration enabled the simultaneous consideration of both factor weights and their fuzzy performance values, ensuring that high-risk policyholders are effectively distinguished from lower-risk groups. The proposed framework was validated through a real-world case study in the non-life insurance sector. By integrating the strengths of FBWM and fuzzy Pareto analysis, the framework provides an original and rigorous methodology for risk assessment in non-life insurance, contributing to both academic research and practical applications in the domain of sustainable insurance management.
This work builds on hypergraphs—graphs whose edges can link any number of vertices—and superhypergraphs, which add a recursive, hierarchical powerset structure to hyperedges. It reviews four practical hypergraph variants: Knowledge Hypergraphs (for multi‐relational knowledge representation), Multimodal Hypergraphs (for combining different data modalities), Lattice Hypergraphs (for spatial and topological modeling), and Hyperbolic Hypergraphs (for embedding vertices in hyperbolic space to capture hierarchies). The paper then shows how to elevate each of these into the superhypergraph framework—resulting in Knowledge SuperHypergraphs, Multimodal SuperHypergraphs, Lattice SuperHypergraphs, and Hyperbolic SuperHypergraphs—and outlines their core properties. Overall, it offers a unified, more expressive modeling approach that paves the way for future advances in both hypergraph and superhypergraph research.
As the most widely played and commercially influential sport worldwide, football (soccer) demands increasingly data-driven and methodologically sound decision-making across tactical, operational, and financial domains. In recent years, Multi-Criteria Decision-Making (MCDM) methods have been increasingly adopted to address the complex, multi-dimensional challenges faced by stakeholders in the sport. To comprehensively examine the current state of research, a systematic literature review (SLR) was conducted focusing on the application of MCDM techniques in football-related decision contexts. The analysis was performed using articles indexed in the Scopus and Web of Science databases, with the Novelty, Impact, Relevance, and Prestige (NIRP) method employed to filter and prioritize the most impactful publications. A final portfolio of 27 articles published between 2000 and 2024 was identified and examined. The selected works were analyzed to identify prevailing MCDM techniques, thematic concentrations, and methodological trends within the domain, providing a comprehensive overview of developments in this field. This review is expected to serve as a foundational reference for academics and practitioners seeking to leverage decision-making frameworks in the evolving landscape of football analytics.
The corporate financial performance of Turkish insurance companies was evaluated through the development of a novel hybrid multi-criteria decision-making (MCDM) framework, integrating the Ranking Comparison (RANCOM), Simple Weight Calculation (SIWEC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) methodologies. Within this framework, financial indicators were selected based on expert input, and indicator weights were determined through the combined application of RANCOM and SIWEC methods. Subsequently, company rankings were established by employing the MAIRCA method. To ensure the robustness and reliability of the proposed framework, extensive sensitivity analyses were conducted. The findings identified the current ratio, defined as the ratio of current assets to current liabilities, as a critical determinant of financial performance. Türkiye Sigorta was consistently ranked as the top-performing company over the analyzed period. The outcomes of the sensitivity analyses confirmed the stability and effectiveness of the proposed decision-making model in assessing corporate financial performance within the insurance industry. This study contributes to the financial performance evaluation literature by demonstrating the applicability and advantages of hybrid MCDM approaches in dynamic and highly regulated sectors such as insurance.
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