Browsing by Author "Bacalum S."

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    Determination of Logistics Performance of G20 Countries Using Quantitative Decision-Making Techniques
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pehlivan P.; Aslan A.I.; David S.; Bacalum S.
    Today, the increase in competition with globalization has caused logistics to gain importance, with international trade as one of its basic elements. Developments in the transportation and logistics sector affect economic growth through their effects on production, consumption, and trade. Similarly, international trade and economic growth also support the development of the transportation and logistics sector. From this perspective, logistics is an indicator of development. Nowadays, logistics is a constantly developing and growing sector. The aim of this study is to conduct performance rankings and cluster analyses of G20 countries in 2023 and to compare the results with the logistics performance index (LPI) scores published by the World Bank. Our assumption is that the results of the analysis and the LPI index would be the same or similar. The findings obtained as a result of both analyses are largely similar to the LPI ranking presented by the World Bank. © 2024 by the authors.
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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.