Yazar "Deveci, Muhammet" seçeneğine göre listele

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    Strategic location analysis for offshore wind farms to sustainably fulfill railway energy demand in Turkey
    (Elsevier Ltd, 2024) Önden, İsmail; Kara, Karahan; Yalçın, Galip Cihan; Deveci, Muhammet; Önden, Abdullah; Eker, Mert
    Providing the energy demands of various sectors through the utilization of renewable energy sources offers a more sustainable and long-term approach compared to fossil fuel consumption. In this study, the proposition is made to address Turkey's rail industry energy demand through Offshore Wind Farms (OWF). Accordingly, the spatial analysis for determining the most suitable OWF location is explored. The research methodology is conducted based on a Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) hybrid approach. Alternative OWF locations are identified using GIS. Location selection criteria are established based on literature review, and a fuzzy-based decision matrix is constructed, incorporating expert opinions. To leverage two distinct fuzzy number sets, Intuitionistic Fuzzy Weighted Averaging (IFWA) and Spherical Fuzzy Stepwise Weight Assessment Ratio Analysis (SF-SWARA) methods are employed for criteria weighting, followed by the aggregation of criterion weights using the Einstein Operations t-norm technique. To ensure comparable OWF alternative rankings, five different alternative ranking methods are employed to obtain alternative rankings. Ultimately, the most suitable OWF location for meeting the energy demand of the rail industry is determined to be the Izmir region. The research findings contribute to managerial perspectives by proposing recommendations concerning strategic location selection, optimized resource allocation, risk mitigation and policy alignment and sustainable long-term planning. © 2023 Elsevier Ltd
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    Supplier selection of companies providing micro mobility service under type-2 neutrosophic number based decision making model
    (Pergamon-Elsevier Science Ltd, 2024) Onden, Ismail; Deveci, Muhammet; Kara, Karahan; Yalcin, Galip Cihan; Onden, Abdullah; Eker, Mert; Hasseb, Mouad
    Environmental approaches play an active role in the production platform as well as in the supply chain. Studies that deal with supplier selection problems in the supply chain with a green focus have recently intensified. In this research, the supplier selection problem of companies providing last mile service, which is seen as the last step of transportation, is handled with a green focus. The supplier selection problem of these companies is differentiated according to strategic and non -strategic product groups. In this study, a green -oriented supplier selection proposal is presented for both the strategic product group and the non -strategic product group. The supplier selection problem is accepted as a multi criteria decision making problem (MCDM). Thus, the type -2 neutrosophic numbers (T2NNs) - based CRiteria Importance Through Intercriteria Correlation (CRITIC) - based Multi -Attributive Border Approximation Area Comparison (MABAC) hybrid model is applied to evaluate the green -oriented supplier. The motivation of the study is to determine the best alternative supplier type for strategic and non -strategic products groups. In this context, two cases are created, ten criteria are selected, and three supplier types are determined. Supplier types are as follows: (i) Classical suppliers, (ii) Hybrid -Classical to green centric suppliers, and (iii) Start -Ups & green centric suppliers. To devise a solution for addressing the research problem, the selection of the optimal green supplier type was accomplished through a recommended T2NNCRITIC-MABAC hybrid model. Accordingly, for both strategic and non -strategic product groups, micromobility service providers identified Hybrid -Classical to Green Centric Suppliers as the most suitable green supplier type. In addition, the findings are strengthened by creating twenty-two sensitivity analysis scenarios. In conclusion, the T2NN-CRITIC-MABAC hybrid method introduced in this study represents a methodologically diverse, fuzzy logic -based approach with practical implications for supplier selection in the micro mobility services sector. Its application and empirical testing contribute to the literature and provide valuable insights for decision -makers in the industry.
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    Sustainable brand logo selection using an AI-Supported PF-WENSLO-ARLON hybrid method
    (Pergamon-Elsevier Science Ltd, 2025) Kara, Karahan; Ergin, Elif Akagun; Yalcin, Galip Cihan; Celik, Tugce; Deveci, Muhammet; Kadry, Seifedine
    Brands use logos to reflect their identities and convey their company character to target customers. Logos not only represent brand value but also provide various information about brands. Companies aiming to emphasize sustainable brand value also seek to showcase this aspect in their logos. Therefore, sustainable brand design and development are preferred. The focus of this research is on sustainable brand development through Artificial Intelligence-supported logo design and developing a decision support system for selecting the best logo among Artificial Intelligence-generated logos. In this study, Artificial Intelligence-supported logo design is explained, and the Multi Attribute Group Decision Making approach is adopted for identifying the best logo among the obtained logos. The Picture Fuzzy - Weights by Envelope and Slope - Alternative Ranking using Two-Step Logarithmic Normalization hybrid method is proposed for logo decision-making, combining the Weights by Envelope and Slope criterion weighting method using picture fuzzy sets with Alternative Ranking using Two-Step Logarithmic Normalization alternative ranking methods. Additionally, the Picture Fuzzy Yager Weighted Average aggregation operator based on Yager t-norm and t-conorm operations is employed. The steps of the hybrid method are outlined, and its algorithm is formulated. To test the applicability and robustness of the algorithm, sustainable logo design for an architectural firm is developed and executed as a real-case study. The research concludes that Artificial Intelligence-supported sustainable logo design can be achieved, and the hybrid method can support determining the best logo based on expert opinions. Based on the research findings, the hybrid method is recommended for sustainable logo design and selection.