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    Optimizing Wind Energy Technology Selection Under Uncertain and Incomplete Information Using Fuzzy Best Worst Method and Fuzzy Information Axiom
    (Old City Publishing Inc, 2024) Cebi, Selcuk; Cem, Ecem; Unal, Gorkem; Karakurt, Necip Fazil
    Technology has a pivotal role in wind energy production, encompassing turbine design, control systems, and grid integration solutions. However, selecting the optimal technology investment presents a multifaceted challenge due to rapid industry evolution. Site-specific considerations, economic viability, reliability, durability, and integration with existing infrastructure all weigh heavily in the decision-making process. Environmental and societal impacts are rigorously supposed to be assessed for responsible energy production. A comprehensive approach, including a thorough evaluation of vendor and supplier capabilities, is deemed indispensable. To address these complexities, this study introduces an innovative approach to optimize wind turbine selection within established locations. The proposed methodology integrates the Fuzzy Best Worst Method (FBWM) and the Information Axiom, chosen for their adaptability in handling subjective expert responses. This combination aligns seamlessly with the nuanced nature of wind turbine technology assessment. The study offers a comprehensive review of relevant multi-criteria decision-making techniques, elaborates on the FBWM and Fuzzy Infor mation Axiom (FIA) approach, and presents apractical application. In the study, operational cost, maintenance cost, and power curve emerge as pivotal criteria. Ultimately, the study provides a robust framework for making informed and impactful technology investments in wind energy production.
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    Risk Analysis in the Food Cold Chain Using Decomposed Fuzzy Set-Based FMEA Approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Arslan, Özlem; Karakurt, Necip; Cem, Ecem; Cebi, Selcuk
    Risk analysis is employed across various domains, including the increasingly vital food supply chain, particularly highlighted by the COVID-19 pandemic. This study focuses on applying decomposed fuzzy sets (DFS), a novel extension of intuitionistic fuzzy sets, within the context of the food cold chain. The objective is to develop “Decomposed Fuzzy Set-Based FMEA (DF FMEA)” by extending the well-known failure mode and effect analysis (FMEA) method to DFS, to assess risks in the food cold chain. The functional and dysfunctional questions related to the severity, occurrence, and detectability of the identified risks; they were addressed to three experts working on the food cold chain. The purpose is to prevent an inconsistent assignment considering the uncertainty and indecision of decision makers. Due to the implementation of the DF FMEA, the identified risks were prioritized as follows: “Financial Risks” held the highest priority, followed by “Delivery Risks”, “Technological Ability Risks”, “Environmental Risks”, “Quality Risks”, and “Social Risks” with the lowest priority. The study’s practical impact lies in the innovative risk assessment method. By considering decision makers’ preferences and uncertainties, the DF FMEA approach enhances informed decision making. This contributes to a robust framework for addressing risks in the food cold chain, aiding practitioners in more effective risk management. © 2023 by the authors.