Browsing by Author "Züngün D."

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    Agro-food brands - Survival and success on Romanian market
    (International Business Information Management Association, IBIMA, 2018) Bratoveanu D.-B.; Züngün D.; Stanciu S.
    The transition to a market economy, by changing the Romanian political regime, favoured the Romanian food brands, which were the first in the preferences of the domestic consumers, who did not have too many options in the centralized economy. Although they had several advantages, which would have allowed them to easily dominate the market, local brands lost their market share in competing with foreign brands, against the backdrop of the higher quality and lower price of the external producers which entered the local market. The research has highlighted a decline in traditional Romanian brands, amid the emergence of new brands associated with food production and trade. The results of the research are useful for the domestic food producers, who can take advantage of increasing the confidence of the Romanian consumer in the national agro-food brands and production. Copyright © 2018 International Business Information Management Association (IBIMA).
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    A study on developing a communicative rational action scale
    (MDPI AG, 2021) Çamlı A.Y.; Virlanuta F.O.; Palamutçuoğlu B.T.; Bărbuță-Mișu N.; Güler Ş.; Züngün D.
    The aim of this study is to develop a Communicative Rational Action Scale and analyze its validity and reliability. The scale has been prepared for all administrators and especially for firm administrators based on Max Weber’s rationalizing theory and Jürgen Habermas’ communicative action theory. The scale reveals to what extent administrators’ behaviors are communicatively rational while deciding or acting. In total, 282 participants joined this study. The sample group consisted of senior administrators of 87 firms acting in Turkey’s different Organized Industrial Zones or Free Zones. Data were analyzed by the SPSS 21 and AMOS 22 programs. Exploratory factor analysis and confirmatory factor analysis were applied to the obtained data. In order to test item discrimination, total item correlations were calculated and items under the value of 0.40 were removed from the scale. Exploratory factor analysis revealed 21 articles and five factors. The correlation coefficient of the 21-article scale with a similar scale is 0.979 (p < 0.001). The Cronbach’s alpha value is 0.945 and the test-retest correlation parameter is r = 0.793 (p < 0.001). In conclusion, it was determined by confirmatory factor analysis that the Communicative Rational Action Scale has a good cohesion criterion, and it is a valid and reliable assessment instrument. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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    Solution of the Capacity-Constrained Vehicle Routing Problem Considering Carbon Footprint Within the Scope of Sustainable Logistics with Genetic Algorithm
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Palamutçuoğlu B.T.; Çavuşoğlu S.; Çamlı A.Y.; Virlanuta F.O.; Bacalum S.; Züngün D.; Moisescu F.
    One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO2 emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO2 emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO2 emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO2 emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO2 emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO2 emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO2 emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP. © 2025 by the authors.