Yazar "Polat, Mustafa" seçeneğine göre listele

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    An integrated neutrosophic Schweizer-Sklar-based model for evaluating economic activities in organized industrial zones
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Yalcin, Galip Cihan; Simic, Vladimir; Polat, Mustafa; Pamucar, Dragan
    Organized industrial zones (OIZs) are specialized areas where industrial activities are concentrated, and various economic activities take place collectively. Within OIZs, diverse economic activities coexist, forming shared industrial spaces. However, different regions tend to cluster different economic activities within OIZs. Given the significance of determining suitable economic activities for OIZs, developing a model for this decision problem, and testing its practical applicability is crucial. This research presents a multi-criteria decision-making approach to develop an economic activity selection model for OIZs. This model is based on type-2 neutrosophic numbers (T2NNs). Furthermore, two T2NNs aggregation operators, namely T2NN Schweizer-Sklar weighted arithmetic mean and T2NN Schweizer-Sklar weighted geometric mean aggregation operators, are developed based on the Schweizer-Sklar operations for use in determining decision-maker weights and ranking economic activities. The T2NN-based criteria importance through intercriteria correlation (CRITIC) method is employed for criterion weighting. An algorithm specific to the proposed model is devised for ranking economic activities. As a case study, this algorithm is applied to the Artvin-Arhavi OIZ, which is in the process of establishment in Turkey. Sensitivity and comparative analyses are conducted to assess the robustness of the developed aggregation operators and the model. In the case study findings, the most significant criterion is identified as investment risk, and forestry, logging, and related service activities are determined as the best economic activity. This research contributes to the field by introducing the model and novel aggregation operator for the selection of economic activities in OIZs.
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    Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis
    (2022) Polat, Mustafa; Kara, Karahan; Yalçın, Galip Cihan
    The logistics industry is among the industries that affect carbon dioxide\remissions. The logistics activities of the countries produce CO2 emissions. For this\rreason, there is a significant relationship between the logistics performance of countries\rand their CO2 emissions. In this study, it is aimed to make a cluster analysis by\rconsidering the CO2 emission per capita efficiency of the countries and their logistics\rperformance. The empirical study was completed in three stages. In the first stage,\rhierarchical clustering analysis was conducted with the logistics performances of the\rcountries and the CO2 emission per capita. In the second stage, the CO2 emission per\rcapita efficiency based on the logistics performance sub-indicators of the countries were\rdetermined by data envelopment analysis. In the third stage, non-hierarchical clustering\ranalysis was performed with the variables of logistics performances and CO2 emission\rper capita efficiency of the countries. 2018 logistics performance index (LPI) and CO2\remission per capita data of 150 countries were used. According to the research findings,\rthere are differences in the findings of hierarchical clustering analysis and nonhierarchical\rclustering analysis. In the conclusion part of the study, the differences\rbetween the clusters were explained and suggestions were developed for the\rresearchers.
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    Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Acar, Avni Zafer; Polat, Mustafa; Onden, Ismail; Yalcin, Galip Cihan
    The green performance values of businesses are of great importance in terms of sustainability, which includes long-term economic, social, and environmental effects. Thus, today, enterprises and managers are increasingly interested in this issue, and related topics, including supplier selection, have been inserted into decision-making procedures. However, how to predict the effects of green performance criteria, which represent environmental sustainability, on social and economic sustainability remains unclear. In this regard, the main purpose of this research is to develop a supplier selection methodology considering green performance criteria by applying multiple regression analysis and the Evidential Fuzzy Multi-Criteria Decision Making (F-MCDM) method based on Dempster-Shafer Theory (DST), which are both powerful methods in statistical analysis and decision-making under uncertainty. In the first phase of the research, variables that significantly affect green performance have been determined by testing the eight generated hypotheses with multiple regression analysis. Then, the best supplier was determined using those green supplier selection criteria in the Evidential F-MCDM method. Since using environmentally hazardous paints in the production process continues, the automobile paint production sector has been chosen as the application area of this green-based supplier selection methodology. In this respect, green dynamic capacity, green purchasing, eco-design, investment recovery, and green product innovation variables have been inserted into the Evidential F-MCDM method as the determinant variables of green performance. This research reveals that integrating multiple regression and Evidential F-MCDM methods can be a hybrid methodology in supplier selection. Thus, a different perspective is introduced into the green supplier selection decision-making process by considering the effects of criteria in the MCDM model on green performance. This innovation enhances the criteria determination and selection processes in classical MCDM approaches. In addition, green dynamic capacity is the most critical criterion in supplier selection based on their green performance, especially in the scope of this research.