A large consumption of natural gas accompanied by reduced production capabilities makes Europe heavily dependent on imports from Russia. More than half of Russian gas is exported by transiting Ukraine, so in the context of the underlying conflict between the two, this is considered uncertain. Therefore, in this article, we modeled the natural gas transportation system using cooperative game theory in order to determine the bargaining power of the major players (Russia, Ukraine, Germany, and Norway) by using a form of the Shapley value. We described the interaction between countries as network games where utilities from transport routes are considered and proposed three scenarios where the gas flow from Russia to Ukraine is either diminished or completely interrupted, with the purpose of finding out how the bargaining power on this market is shifted in case of network redesign. In this context, we included in the analysis the scenario where the Nord Stream 2 pipeline will be finished. Results showed that Russia dominates the market in any scenario, and by avoiding Ukraine, its position is even further strengthened. Moreover, Germany’s position remains stable considering its diverse imports and large storage capabilities, and its bargaining power increases in the case of diminishing or avoiding the Ukrainian gas pipelines.

In the dynamic landscape of modern business operations, ensuring economic security through efficient and intelligent fleet management is imperative. This necessitates a dual focus on safeguarding revenue streams and optimizing operational costs. The aim of this study centers on two main objectives: first, to identify driving behaviors that have a substantial impact on vehicle maintenance costs; second, to ensure the sustainability of the fleet is managed effectively. To achieve these objectives, the research employs unsupervised Machine Learning (ML) techniques for segmenting driving styles based on diverse parameters collected from Internet of Things (IoT) devices. Furthermore, the Long Short-Term Memory (LSTM) algorithm is used for forecasting fuel consumption, offering a predictive glance into future expenditures. The methodology is based on the analysis of data gathered from sensors installed on the vehicle's Controller Area Network (CAN), collected over a span of five months. The findings spotlight a subset of drivers whose aggressive driving significantly influences maintenance costs and highlight optimal indicators for drivers to monitor to minimize CO2 emissions. Additionally, the study identifies key performance indicators that drivers should monitor to reduce CO2 emissions, contributing to the environmental sustainability of the fleet. This investigation not only elucidates the financial and environmental implications of driving behaviors but also showcases the transformative potential of ML technologies in enhancing the strategic management of vehicle fleets. Through this exploration, the research advocates for the integration of advanced analytics and sustainable practices as foundational elements for businesses striving to achieve economic security and operational resilience.

International competition between retailers is very intense in actual world and the capacity to remain profitable is essential influenced by the ability to forecast the demand as accurately as possible. Also, is very important to minimize stock outages, to reduce the volume of each product in stock, to reduce costs and to increase the revenues. The current work proposes a new approach to forecasting and optimization in the retail environment and introduces an improved method for identifying the optimal algorithm that can help a retailer to get as close as possible to mentioned conditions. The research methodology identifies the optimal forecasting model and then optimize the model results and parameters such as the optimal order quantity, the reordering point, the maximum stock and the safety stock. To obtain the optimal solutions we develop distinct forecasting and optimization algorithms, starting with classical econometric methods like ARIMA, or dynamic optimization, and progressing to more complex approaches, including transformers, neural networks, Fourier transformation, or combined algorithms like Fourier transformation with Prophet. An important challenge was identifying an optimal method for selecting the best operating algorithm, as classical methods like RMSE, MAPE, or MAE failed to identify the most accurate prediction algorithm. In order to test the algorithm’s quality, we test twenty-six distinct forecast algorithms and compared at each product level, identifying Fourier transformation, either on its own or in combination with other algorithms, as a highly effective forecasting model. Finally, the Continuous Ranked Probability Score is proposed as the best model selection strategy.