Yazar "Pamucar, Dragan" seçeneğine göre listele

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    Fermatean fuzzy framework based on preference selection index and combined compromise solution methods for green supplier selection in textile industry
    (Taylor & Francis Ltd, 2024) Pamucar, Dragan; Ulutas, Alptekin; Topal, Ayse; Karamasa, Caglar; Ecer, Fatih
    This work aims to enhance the efficiency and dependability of the green supplier selection process by developing a decision support tool. Thereby, a novel fuzzy group multi-criteria decision-making approach based on the Preference Selection Index (PSI) and Combined Compromise Solution (CoCoSo) methods under the Fermatean Fuzzy (FF) environment for green supplier selection is proposed. The primary novelties and contributions are: (i) for the first time, the FF-PSI model is developed to extract the priority weight values of criteria, (ii) an improved FF-CoCoSo methodology is proposed, and (ii) the FF-PSI-CoCoSo framework is offered for resolving challenging real-life problems. To present the framework's practicality, a real-case study evaluating six suppliers concerning nine drives under economics, environmental, and technological aspects in a textile company in Turkey is conducted. The results indicate that green warehousing is the most essential driver for green suppliers, followed by recycling and damaged product ratio. A detailed sensitivity and comparison check is further conducted to display the solidity and effectiveness of the proposed framework. The suggested framework offers a reliable solution for green supplier selection and a precise sustainable rating of suppliers. Moreover, it can be addressed to solve various challenging real-world problems.
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    A New Integrated Multi-Criteria Decision-Making Model for Sustainable Supplier Selection Based on a Novel Grey WISP and Grey BWM Methods
    (Mdpi, 2022) Ulutas, Alptekin; Topal, Ayse; Pamucar, Dragan; Stevic, Zeljko; Karabasevic, Darjan; Popovic, Gabrijela
    Supplier selection is an important task in supply chain management, as suppliers have a vital role in the success of organisations in a supply chain. Sustainability has emerged as a solution to decreasing resources and increasing environmental and social problems in the past few decades. It has been applied to various industrial operations, one of them is supplier selection, to mitigate unwanted effects in the future. Sustainable supplier selection is a complicated multi-criteria decision making problem, including several criteria from economic, environmental, and social perspectives. To deal with subjective judgements of decision makers, fuzzy and grey methods are widely used in multi-criteria decision making, In the case of small, limited, and incomplete data, the grey theory provides satisfactory results, compared to fuzzy methods. Therefore, this study is an integrated method including grey Best-Worst Method (BWM) and grey Weighted Sum-Product (WISP) for choosing the most sustainable supplier for a textile manufacturer, which includes three main criteria and twelve sub-criteria. According to the result of the proposed model, the supplier with the best performance was determined to be the supplier with the SP2 coded. The results of the developed model were shown to the experts, and the accuracy of the results was confirmed. According to the experts, a higher amount of product can be purchased from the supplier with the SP2 code, and a tighter relationship can be worked with this supplier. The contributions of this study are: (1) Develop a new grey MCDM model called Grey WISP. (2) Create a new integrated MCDM model with grey theory, BWM, and WISP methods that can be applied to assess supplier sustainability using this hybrid model. The proposed model can be used not just for selecting sustainable suppliers, but also for any other decision problems that have multiple criteria and alternatives. The findings suggest that the Grey WISP method achieved accurate results.
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    Virtual reality headsets for employee training in enterprises: fuzzy SRP data-driven framework for a comprehensive evaluation
    (Springer London Ltd, 2026) Aycin, Ejder; Asan, Hakan; Ecer, Fatih; Gorcun, Omer Faruk; Ulutas, Alptekin; Pamucar, Dragan
    Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research's novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.