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    Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model
    (Pergamon-Elsevier Science Ltd, 2024) Ulutas, Alptekin; Topal, Ayse; Gorcun, Omer Faruk; Ecer, Fatih
    Automotive businesses often delegate logistical tasks to third-party logistics (3PLs) service providers to acquire a competitive edge in the dynamic market. Nevertheless, selecting the most suitable third-party logistics (3PL) partner is a multifaceted undertaking that needs careful evaluation of several criteria and alternatives. This research aims to introduce an integrated grey Multiple Criteria Decision Making (MCDM) framework for automotive businesses to deal with the multidimensional 3PL selection decision problem. This framework incorporates an enhanced Preference Selection Index (PSI), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and Mixed Aggregation by Comprehensive Normalization Technique (MACONT). The LOPCOW-G and grey PSI (PSI-G) methods extract the criterion weights, whereas the MACONT-G method ranks the alternatives. The suggested framework's practicality is shown by conducting a case study about evaluating and selecting a third-party logistics (3PLs) provider. The findings indicate that the parameters of significant importance are skilled workforce (0.0977), financial strength (0.0901), and IT-IS competence (0.0839). Furthermore, TPL4 has been recognized as the most optimum option with a value of 0.4797. The MACONT-G model is as well compared against other grey MCDM techniques to assess the validity of the proposed model. The Pearson correlation coefficient between MACONT-G and the other models based on grey sets is 0.958, suggesting a significant and positive link. Furthermore, it is worth noting that a sensitivity analysis has been conducted to validate the accuracy and reliability of the created framework. In conclusion, this study has identified managerial and policy implications that might assist policymakers and executives in effectively evaluating 3PL providers.
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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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    Green-resilient supplier selection via a new integrated rough multi-criteria framework
    (Elsevier, 2025) Ulutas, Alptekin; Topal, Ayse; Ecer, Fatih
    Initially, companies focused solely on the economic aspects of business processes; now, they have begun to prioritize environmental and social issues to mitigate adverse impacts on the ecology and community. Furthermore, resilience is crucial in ensuring the supply chain remains uninterrupted. Therefore, evaluating the suppliers' sustainability and resilience performance is paramount. A unique mathematical tool is required to integrate resilience and sustainability considerations into supplier selection decisions. Hence, the research fulfills this necessity by introducing a novel, multi-criteria rough methodology. The studies in the literature primarily assess suppliers from either a green or resilient perspective, employing fuzzy MCDM methods to address uncertainty. However, they struggle to cope with uncertainty when faced with limited information. To address this gap, this study proposes a novel approach based on rough set theory to handle interpersonal ambiguity and vagueness flexibly without requiring additional information. It determines the weights of criteria used for greenresilient supplier selection and evaluates the green-resilient performance of suppliers. To this end, rough logarithmic percentage change-driven objective weighting and rough maximum of criterion frameworks are developed to determine criteria weights, whereas the rough mixed aggregation by the comprehensive normalization technique model is designed to decide alternative rankings. This approach requires less prior information than fuzzy set-based methodologies and offers additional flexibility in handling imprecision. To demonstrate its practicality, a real case study from a garment-textile factory in Turkey is presented. The work is the first study of this issue to employ the introduced methodology. Findings highlight that the impact on the local community is the foremost driver for green-resilient supplier selection, followed by cost and supplier sustainability. The model's reliability is validated by comparative and sensitivity analysis. The research contributes to the field by providing a reliable tool that combines rough sets with resilience and sustainability approaches, thus improving the effectiveness and credibility of supplier selection activities in engineering. The work provides executives with an effective supplier evaluation process that jointly addresses sustainability and resilience assessments under uncertainty.
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    MULTIPLE NORMALIZATION RATING ANALYSIS (MUNRA) AND ITS APPLICATION TO DIGITAL SUPPLIER SELECTION IN THE TEXTILE INDUSTRY
    (Vilnius Gediminas Tech Univ, 2025) Ulutas, Alptekin; Ecer, Fatih; Turskis, Zenonas
    The rapid development of digital technologies-such as IoT, AI, blockchain, and digital twins-has transformed supply chains into interconnected ecosystems, making digital supplier selection both critical and complex. For the first time, this study proposes a novel multi-criteria decision-making (MCDM) method, Multiple Normalization Rating Analysis (MUNRA), for ranking alternatives. It integrates linear, vector, and non-linear normalization to improve robustness, reduce rank reversal, and enhance decision accuracy. A case study of digital supplier selection in the textile industry is considered for a real-life application of the method. Results highlight technology integration, flexibility, and technological capability as the most influential criteria for selecting digital suppliers. Moreover, the final ranking of the six digital suppliers is as follows: DS5, DS4, DS2, DS6, DS1, and DS3. Validation through comparative MCDM methods, Spearman correlation, and sensitivity analyses confirms the credibility of the method. It is also shown that it is free from the rank reversal phenomenon. The research presents a computationally efficient and rigorous method for evaluating digital suppliers, offering strategic insights for digital supply chain management. The application of MUNRA to a larger decision-making problem further illustrates its scalability and cross-domain applicability.
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    Telescopic forklift selection through a novel interval-valued Fermatean fuzzy PIPRECIA-WISP approach
    (Pergamon-Elsevier Science Ltd, 2024) Gorcun, Omer Faruk; Ulutas, Alptekin; Topal, Ayse; Ecer, Fatih
    Telescopic forklifts stand apart from other forklift types, boasting numerous benefits. They offer notable advantages, such as enhanced manoeuvrability, accessibility to elevated areas, versatility, and the capacity to operate at high speeds. Choosing appropriate telescopic forklifts can substantially enhance operational efficiency and efficacy within the industry. Concurrently, it can bolster business competitiveness by expediting logistical processes, yielding notable cost reductions. Nonetheless, the intricacy and specificity inherent in these machines complicate the decision-making process for stakeholders. Furthermore, conflicting criteria, the continuous evolution of manufacturers' models, and the industry's intricate nature compound the selection challenges. Hence, there is a pressing need for a resilient, dependable, and practical decision-making framework capable of adeptly navigating uncertainties to yield reliable and coherent outcomes. This study aimed to develop an integrated decision-making model based on interval-valued Fermatean fuzzy (IVFF) sets to respond to these requirements and address this critical decision-making problem in the relevant industry, which is also significantly affected by complex uncertainties. This work, therefore, introduces an integrated methodology for decisionmaking, IVFF-PIPRECIA and IVFF-WISP techniques. IVFF-PIPRECIA determines criteria importance weights, whereas IVFF-WISP identifies optimal alternatives. A case study in the textile industry validates the framework's practicality. Purchase price emerges as the primary criterion, exceeding 500 thousand euros for telescopic forklifts, followed closely by load-carrying capacity. The second alternative proves to be the best option. Comparative and sensitivity analyses confirm the model's credibility. The approach effectively handles decisionmaking uncertainties, yielding competitive outcomes. It can be applied to diverse engineering problems. Insights from this study assist users in selecting optimal equipment and may inform forklift manufacturers in improving machinery. Future research could focus on establishing real-time operational data collection frameworks for these vehicles.
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    The Alternative Prioritization and Assessment System (ALPAS) Method for Environmental Performance Evaluation
    (Mdpi, 2025) Ulutas, Alptekin; Topal, Ayse; Ecer, Fatih
    This study aims to evaluate the environmental performance of G7 countries using the Environmental Performance Index. To do this, we introduce a novel ranking multi-criteria method, Alternative Prioritization and Assessment System, for the first time in the literature. It offers a useful contribution to the multi-criteria decision-making field by tackling several ranking problems, such as low interpretability, a lack of dual evaluation metrics, and limited flexibility in data-driven scenarios. Moreover, three advanced multi-criteria decision-making weighting methods are used to assign weights to the environmental performance criteria. Therefore, the proposed Alternative Prioritization and Assessment System-based methodology evaluates the environmental performance of G7 countries in reaching sustainable development goals. The results show that the waste recovery rate is the paramount indicator, while unsafe drinking water has the least significance. Germany is ranked as the top-performing country, while Japan is ranked lowest. The key contribution of this research lies in the development and implementation of the Alternative Prioritization and Assessment System method, offering enhanced ranking stability, transparency, and dual-perspective evaluation. The use of the Environmental Performance Index further supports replicability and policy relevance. The proposed model can guide environmental policy formulation and benchmarking efforts among industrialized nations. It also provides a robust framework for cross-national sustainability comparisons in future research.
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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.