Yazar "Demir, Guelay" seçeneğine göre listele

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    Efficient Decision Making for Sustainable Energy Using Single-Valued Neutrosophic Prioritized Interactive Aggregation Operators
    (Mdpi, 2023) Riaz, Muhammad; Farid, Hafiz Muhammad Athar; Antucheviciene, Jurgita; Demir, Guelay
    To reduce greenhouse gas emissions, conserve the environment, and reduce dependency on fossil fuels, the transition from fossil energy to renewable energy is deemed essential. Several companies around the globe, especially big conglomerates, were pioneers in the use of renewable energy. For sustainable growth, Pakistani businesses are growing increasingly interested in the use of green sources in manufacturing and economic activities. In recent years, there has been a growth in the number of companies that are eager to use renewable energies to produce products that correspond to green standards, therefore boosting their competitiveness. Yet, the selection of an appropriate energy source for any industrially complex project is not a simple task, as numerous qualitative and quantitative characteristics must be considered. To arrive at a feasible conclusion, this research provides a multi-criteria paradigm for sustainable energy selection in a single-valued neutrosophic environment. This work developed an innovative aggregation operators approach that interprets the input evaluation using single-valued neutrosophic numbers. For this, a single-valued neutrosophic prioritized interactive weighted averaging operator and single-valued neutrosophic prioritized interactive weighted geometric operator has been introduced. Several additional appealing features of these aggregation operators are also discussed. The application of the recommended operators for sustainable energy related to the industrial complex is discussed. A comparison analysis proves the empirical existence of the suggested methodology's consistency and superiority.
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    Identification of Industrial Occupational Safety Risks and Selection of Optimum Intervention Strategies: Fuzzy MCDM Approach
    (MDPI, 2025) Demir, Guelay; Bouraima, Mouhamed Bayane; Badi, Ibrahim; Stevic, Zeljko; Das, Dillip Kumar
    Over 1.1 million deaths occur annually from workplace injuries and diseases, with higher risks in developing countries. Occupational safety studies commonly use quantitative or qualitative methods, but these often fail to address uncertainty. This research targets the Libyan Steel Company (LISCO), aiming to analyze safety risks and develop a structured approach to identify optimal risk mitigation strategies. To this end, the Fuzzy Weights by ENvelope and SLOpe (F-WENSLO) method was chosen to determine the weights of three main safety risks and a total of 18 sub-risks belonging to them, and the fuzzy Bonferroni mean aggregation operator is applied to synthesize expert opinions. The Fuzzy Alternative Ranking Technique based on Adaptive Standardized Intervals (F-ARTASI) method was used to identify and rank the most appropriate safety interventions. While the primary risks identified under the main criteria and sub-criteria are occupational diseases and noise-induced diseases, with weights of 0.4737 and 0.1313, respectively, the intervention strategy deemed most effective for enhancing occupational safety is behavioral safety programs, which hold a weight of 11.0341. The sensitivity test of the analysis results reveals that although the criteria weights and the parameters used in the analysis vary under various scenarios, the ranking of the alternatives remains consistent. Since the general ranking of the alternatives is the same in other methods, decision makers will reach similar results no matter which method they use. This shows that a flexible and reliable decision-making approach is adopted in the process of optimizing occupational safety risks. This research emphasizes the critical importance of prioritizing occupational diseases and natural hazards in the formulation of occupational safety strategies and thus aims to contribute to the protection of workers in industrial plants such as LISCO.
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    Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis
    (Pergamon-Elsevier Science Ltd, 2024) Demir, Guelay; Chatterjee, Prasenjit; Pamucar, Dragan
    In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a sub-stantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual rep-resentation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment, featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely Journal of Cleaner Production, Expert Systems With Applications, and Computers and Industrial Engineering. North China Electric Power University is the leading institute with the highest research outputs. Pamuc ?ar D is the most cited author, with 2594 citations and 39 articles, followed by Kahraman C and Zavadskas EK. This study sheds light on trends in scientific de-velopments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis.
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    Setting a shared development agenda: prioritizing the sustainable development goals in the Dominican Republic with fuzzy-LMAW
    (Nature Portfolio, 2024) Fernandez-Portillo, Luis A.; Demir, Guelay; Sianes, Antonio; Santos-Carrillo, Francisco
    The sustainable development goals (SDGs) were established by the United Nations as an international call to eradicate poverty, safeguard the environment, and guarantee that everyone lives in peace and prosperity by 2030. The SDGs aim to balance growth and sustainability in three dimensions: social, economic and environmental. However, in the post-pandemic era, when resources for public development policies are scarce, nations face the problem of prioritizing which SDGs to pursue. A lack of agreement is one of the determinants of low performance levels of the SDGs, and multicriteria decision analysis tools can help in this task, which is especially relevant in developing countries that are falling behind in achieving the SDGs. To test the feasibility and appropriateness of one of these tools, the Fuzzy Logarithm Methodology of Additive Weights, we apply it to prioritize the SDGs in the Dominican Republic, to see if the priorities established are consistent. Seventeen experts were surveyed, and the main result was that Decent work and economic growth was the most important goal for the country. Our findings, consistent with the literature, show the path to similar applications in other developing countries to enhance performance levels in the achievement of the SDGs.
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    The use of continuous visual aid in the Best-Worst Method: an experiment with organic farmers in Paraguay
    (Springer, 2024) Fernandez-Portillo, Luis A.; Estepa-Mohedano, Lorenzo; Demir, Guelay
    The adoption of organic agriculture in developing countries is a complex decision, as it involves considering various factors. Multicriteria decision analysis (MCDA) methods can be suitable for this type of decision, as evidenced in the literature. The Best-Worst Method (BWM) is an MCDA tool that has proven useful due to its simplicity and computational capability. This research aims to test the applicability of this method to a population with low levels of education and determine whether a questionnaire with a continuous visual aid, a slider, is more suitable than the standard questionnaire with digits. Moreover, it aims to ascertain which factors the consistency of the responses depends on. To achieve this, 217 farmers in Paraguay were surveyed, and the consistency of the results was measured. We found that the questionnaires with digits were more consistent. Then we investigated possible causes of these differences, observing that respondents with sliders tended to concentrate their responses more heavily on the extreme values of the scale (1 and 9). A regression analysis of the consistency values with respect to various socioeconomic variables found only a slight effect of total farm incomes to be significant. These results demonstrate the feasibility of using this sophisticated method in this type of population and suggest that using sliders is not advisable.