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    Assessing Green Logistics and Supply Chain Resilience With Future Importance Analysis: Machine Learning and Multicriteria Decision-Making Approach
    (Wiley, 2026) Simsek, Ahmed Ihsan; Koc, Erdinc; Gultekin Tarla, Esma
    This study investigates the effectiveness of sustainability-oriented factors in supply chain management and their effects on supply chain resilience. Using the Supply Chain Management with Green Logistics dataset obtained from the Kaggle platform, 19 basic supply chain components of 69 companies were examined with machine learning and multicriteria decision-making (MCDM) methods. The modeling performed using Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost, MLPRegressor, Lasso, Ridge, SVR, AdaBoost, and ExtraTrees algorithms was evaluated with performance metrics such as RMSE, MSE, MAE, MAPE, and R 2, and the AdaBoost algorithm showed the best performance. In order to improve the performance of the model, fivefold K-fold cross-validation and hyperparameter optimization with GridSearch were performed. In the feature importance analysis, Order Fulfillment Rate stood out as the variable with the highest impact score, whereas sustainability-oriented variables (recycling rate, carbon emissions and use of renewable energy) were found to be of lower importance. The results obtained from the study show that the most important variable is order fulfillment and customer focus. Within this framework, according to the results obtained for companies that have green-focused processes, traditional supply chain elements are more important. Sensitivity analyses conducted with ADAM, CoCoSo, and MABAC methods examined the effects of changes in the weights of these variables on the results. The findings highlight the limited impact of green logistics practices on the efficiency of enterprises in the short term and show the importance of including these factors in strategic planning processes. This indicates that environmental sustainability should be supported by policy-oriented interventions rather than market mechanisms. In this context, structural policy changes are needed, such as providing tax breaks and appropriate financing opportunities for green logistics investments, as well as encouraging logistics operations with low carbon footprints through certification and providing competitive advantages to these companies.
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    Capacited range coverage location model for electric vehicle charging stations: A case of Istanbul-Ankara highway
    (Elsevier, 2025) Tasdemir, Beste Desticioglu; Koc, Erdinc; Simsek, Ahmed Ihsan
    It is clear that the rate of use of electric vehicles is increasing day by day due to growing environmental concerns of consumers, scarcity of natural resources, increasing awareness of renewable energy sources, following technological progress, and the cost advantage it has for individual or corporate users. One of the most important drawbacks of electric vehicles is their low range. With the studies carried out in battery technology, it is aimed to increase the range distance. However, when the range distances of today's electric vehicles considered, vehicle owners demand to recharge as soon as possible on long distances. While electric vehicle owners have the opportunity to perform many activities during charging in the city, they do not have such opportunities in intercity traffic such as on highways. For this reason, the waiting times for charging stations on intercity roads should be reduced and their availability should be increased. In this study, the Istanbul-Ankara highway, one of the most frequently used highways in Turkey, is considered. It is aimed to reach the minimum number of charging stations that meet the assumptions. The capacited range coverage location model is used to solve a real-life problem by considering the distances between fuel stations and resting facilities on this highway. With the modifications made to the base model, the obtained model has become more suitable for use in real life problems. With the results obtained, it is stated in which gas station or resting facility charging stations should be installed on the highway.
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    Deep Learning Forecasting Model for Market Demand of Electric Vehicles
    (Mdpi, 2024) Simsek, Ahmed Ihsan; Koc, Erdinc; Tasdemir, Beste Desticioglu; Aksoz, Ahmet; Turkoglu, Muammer; Sengur, Abdulkadir
    The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model's ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches.