Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ4/r/munzur/1459
  Smart logistics with a maturity assessment perspective

  Göçmen Polat, Elifcan; Derse, Onur

  The logistics sector is influenced by digitalisation and technological advancement in improving sustainability and efficiency. Logistics companies, which are highly capable of incorporating innovative technologies into their business flows, have made profound impacts, ultimately leading to a transformation in logistics services. In this context, this study presents the current and target maturity levels to make an international logistics company smarter and provides recommendations as an improvement road map for the logistics sector. Within the scope of this research, nine dimensions and 36 maturity items of the smart logistics (SL) concept are discussed to assess the maturity levels using the weighted maturity score calculation model (WMSC), in which evaluation of all maturity items using the capability maturity model (CMM) maturity levels and calculation of the criteria weights using fuzzy DEMATEL (F-DEMATEL) is integrated. Computational results show that warehouse management systems and material flow control systems have lower maturity levels. Thus, product picking by voice, integrating the warehouse management system, ensures real-time updates and data integration, reducing stockouts and overstock.

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• MakaleYalnız künye2025Scopus SJRQ4/r/munzur/1948
  Deep learning-based hybrid traffic flow prediction model for freight transport

  Utku, Anıl; Kaya, Sema Kayapinar; Can, Umit

  Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.

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