Browsing by Author "Tirkolaee, E.B."

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Assessing the Renewable Energy Sources for Sustainable Energy Generation Systems: Interval-Valued Q-Rung Orthopair Fuzzy SWARA-TOPSIS
(Elsevier Ltd, 2026) Faruk Görçün, Ö.F.; Aytekin, A.; Selçuk Korucuk, S.; Tirkolaee, E.B.
Renewable Energy Sources (RESs) help decarbonize power systems, but selecting among them is a challenging decision problem due to multiple, often conflicting, technical, economic, environmental, and health-related criteria. Consequently, numerous studies in the literature have attempted to address this decision-making issue using objective, subjective, and fuzzy decision-making procedures. However, there are still unaddressed research gaps in the literature, particularly regarding the explicit modeling of expert hesitation and ambiguity in real-world RES selection cases. The current study develops a decision-making model based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methods integrated with Interval-Valued q-Rung Orthopair Fuzzy Sets (IV-q-ROFSs) to fill these gaps. Unlike previous studies that have predominantly applied conventional fuzzy MCDM techniques, our model introduces the first integration of IV-q-ROFS into RES selection. This novelty enables a more accurate representation of expert hesitation and uncertainty. The study is applied to a real industrial case in Turkey, where six RES alternatives are evaluated across 43 criteria by five senior experts under the supervision of a three-member professionals’ board. Furthermore, the structured robustness check and systematic literature mapping ensure that the proposed approach is methodologically robust and practically relevant for policymakers and energy planners. The application results of the developed model demonstrate that the estimated energy production potential of the RES and the effects of carcinogens generated from utilizing these energy sources are the critical factors influencing the selection of the most appropriate RESs. Solar energy ranked first among the alternatives. The applicability and validity of the developed model are examined by a comprehensive robustness check consisting of tests of sensitivity, comparison, and resilience to the rank reversal problem. Overall, the study provides (i) a novel methodological framework integrating IV-q-ROFS with SWARA and TOPSIS, (ii) empirical evidence from a comprehensive real-world RES selection case, and (iii) policy-relevant insights into the drivers of renewable energy adoption. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Evaluating Green Marketing Practices in the Logistics Industry Under Type-2 Neutrosophic Fuzzy Environment
(Elsevier Ltd, 2025) Faruk Görçün, Ö.F.; Ul Ain, N.; Kucukonder, H.; Durmusoglu, S.S.; Uray, N.; Tirkolaee, E.B.
The logistics industry is under increasing pressure to implement Green Marketing (GM) strategies in response to growing environmental concerns and rising stakeholder expectations. Although international organizations and governments encourage the adoption of sustainability, practical decision support tools for executing GM strategies, particularly within logistics Small and Medium-Sized Enterprises (SMEs), remain underdeveloped. This study tries to advance the literature by introducing a novel hybrid Multi-Criteria Decision-Making (MCDM) framework that uniquely integrates Delphi, CRiteria Importance Through Inter-criteria Correlation (CRITIC), and Mixed Aggregation by cOmprehensive Normalization Technique (MACONT) methods with Type-2 Neutrosophic Numbers (T2NNs). Unlike prior fuzzy MCDM studies, this integration simultaneously incorporates subjective and objective weighting, preserves ordinal consistency, and explicitly manages higher-order uncertainty. The model is applied to evaluate the GM performance of logistics SMEs in Turkey, identify key evaluation criteria, and rank firms accordingly. Among the evaluated criteria, “Land usage” and “Investment in reducing greenhouse gas emissions” emerged as the most influential, while “Omsan Logistics” is identified as the top-performing firm in GM practices. The model's reliability is then confirmed through a two-phase sensitivity analysis, demonstrating robustness across different scenarios. The findings of this work provide significant implications for logistics managers, policymakers, and researchers aiming to enhance environmental performance and make informed decisions in complex and ambiguous operational environments. © 2025 Elsevier Ltd.