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The paper highlights the potential of cross-docking to reduce storage time and costs. The study addresses evolving market demands that push logistics providers to adopt new technologies for operational efficiency, emphasizing the often-overlooked importance of optimizing cross-docking layouts. The research, conducted in two phases, first analyzed the current warehouse layout (Variant I) to identify inefficiencies and then designed a new layout (Variant II) that was simulated using FlexSim 2022 software. The results showed significant improvements with the new layout, including a 35% increase in deliveries and a 3.23% reduction in forklift travel distances, leading to lower operational costs. Even minor adjustments in the warehouse design proved to enhance logistics efficiency, particularly during peak demand periods like holidays. The study demonstrates how FlexSim software can be applied in cold chain logistics to optimize warehouse operations, underscoring the benefits of cross-docking for cost-effective logistics management.

This article discusses the application of an ANN model for forecasting the fuel consumption of vehicles on the regular city bus lines. In the context of rising fuel costs and their impact on transportation companies, the developed system supports the optimization of fuel consumption standards and fleet management. The model accounts for prediction factors such as route length [km], number of bus stops, probability of traffic jams [from 1—low to 3—high], ambient temperature [°C], from external database, technical state of the vehicle [from 1—good to 5—bad], type of petrol [1—ON; 2—E95], filling of the vehicle/number of passengers [from 1—empty to 5—full]. Based on this these data, the presented model was developed. The system analyzes input, generates reports, and identifies potential issues, including excessive fuel consumption or fuel theft. Its modular design allows for further development and adaptation to user needs. Implementing this solution enhances operational efficiency, reduces costs, and optimizes transportation management.

This article presents a review of the literature related to the topic discussed and then discusses the system enabling the collection of data for analysis, the components of which are described in the paper. Next, a case study containing analyses of the circulation of logistic containers and the quality of deliveries is presented. Finally, a discussion of the results is presented. Background: This research is instrumental in navigating the intricacies of using these insulated containers of disturbances in the cold supply chain is imperative for ensuring the safety of perishable items, pharmaceuticals, and medical provisions, all of which necessitate precise temperature storage. Moreover, it holds significant sway over the efficacy of logistics, curtails losses, and guarantees adherence to regulatory stipulations and quality benchmarks. Research target: The research aimed to analyze the circulation of isothermal containers and indirectly assess the transportation quality for food products using the example of a 3PL company. Method: The article addresses issues encountered in the transportation of products within the cold supply chain. Using a company as an example, the temperature of coolboxes at the beginning of the logistics cycle, temperature distribution during transportation, and the temperature at the end of delivery were analyzed. The circulation of containers, their utilization over time, cycle durations, and the entire container circulation process were also examined. Results: Based on the collected data, recommendations were formulated to enhance product quality and increase container utilization. This research offers valuable insights into improving the efficiency and quality of logistics processes, specifically within the cold chain transportation industry, and provides recommendations for better managing temperature-sensitive product transportation.

The impact of the pantograph of a rail vehicle on the overhead contact line depends on many factors. Among other things, the type of pantograph, i.e., the material of the sliding strip, influences the wear and possible damage to the sliding strip. The possibility of predicting pantograph failures may make it possible to reduce the number of these kinds of failures. This article presents a method for predicting the technical state of the pantograph by using artificial neural networks. The presented method enables the prediction of the wear and damage of the pantograph, with particular emphasis on carbon sliding strips. The paper compares 12 predictive models based on regression algorithms, where different training algorithms and activation functions were used. Two different types of training data were also used. Such a distinction made it possible to determine the optimal structure of the input and output data teaching the neural network, as well as the determination of the best structure and parameters of the model enabling the prediction of the technical condition of the current collector.

Damage to the pantograph or sliding strip may cause the blocking of the railway line. This is the main reason for which the prediction of pantographs’ failure is important for railway carriers and researchers. This article presents a sliding strips failure prediction method as a main means of preventing disruptions to the transport chain. To develop the best predictive model based on the decision tree, the complex tree, medium tree and simple tree machine learning methods were tested. Using a decision tree, the categorization of the given technical conditions can be properly realized. The obtained results showed that the presented model can reduce sliding strip failure by up to 50%. Special attention was paid to the current collector (AKP-4E, 5ZL type), measured during periodic reviews of locomotives EU07 and EU09. To assess the reliability of the selected pantograph strips, a non-destructive degradation analysis was carried out. On the basis of the wear measurements of the strips and the critical value of wear, a failure distribution model was developed. Operational data, collected during periodic technical reviews, were provided by one of the biggest railway carriers in Poland. The results of the performed analyses may be used to build a preventive maintenance strategy to protect pantographs. The applied reliability models of wear propagation can be extended by the parameters of the cost and repair time becoming the basis for estimating the costs of operation and maintenance.

The problem of object localization in indoor environments is very important in order to make a company effective and to detect disruption in the logistics system in real-time. Present research investigates how the IoT (Internet of Things) location system based on Bluetooth can be implemented for this solution. The location based on the Bluetooth is hard to predict. Radio wave interference in this frequency is affected by other devices, steel, vessels containing water, and more. However, proper data processing and signal stabilization can increase the accuracy of the location. To be sure that the location system based on the BT (Bluetooth) can be implemented for real cases, an analysis of signal strength amplitude and disruption was made. The paper presents R&D (Research and Development) works with a practical test in real cases. The signal strength fluctuation for the receiver is between 7 and 10 dBm for ESP32 device and between 13 and 14 dBm for Raspberry. For commercial implementation the number of devices scanned in the time window is also important. For Raspberry, the optimal time window is 5 s; in this time six transmitters can be detected. ESP32 has a problem with detecting devices in a short time, as just two transmitters can be detected in 4–8 s time window. Localisation precision depends on the distance between transmitter and receiver, and the angle from the axis of the directional antenna. For the distance of 10 m the measurement error is 1.2–6.1 m, whilst for the distance of 40 m the measurement error is 4.9 to 24.6 m. Using a Kalman filter can reduce the localization error to 1.5 m.

The optimization of maintenance intervals is crucial for enhancing efficiency, sustainability, and cost-effectiveness in transport operations. This paper presents a method for optimizing maintenance intervals for vehicles in various modes of transport, focusing on minimizing downtime due to repairs and maintenance. By integrating advanced technologies like artificial intelligence (AI) and the Internet of Things (IoT), maintenance intervals are dynamically adjusted using real-time data, resulting in better resource utilization and reduced operational costs. The key findings of this research indicate significant reductions in downtime and maintenance costs, leading to improved efficiency and sustainability across transport modes. Although the case study is based on railway vehicles, the approach is applicable to road, maritime, and air transport as well. By leveraging optimization algorithms, such as machine learning, this solution predicts optimal maintenance timing, thereby reducing resource consumption and improving operational efficiency. The case study on pantograph maintenance demonstrates significant financial savings and reduced waste. This research highlights the benefits of maintenance optimization for sustainability and efficiency across the entire transport sector.

Strokes are currently the third most common cause of death worldwide and the leading cause of disability in people over 50 years of age. The functioning of post-stroke patients depends primarily on well-conducted rehabilitation, both in stationary conditions and at home. The aim of this study was to evaluate the functional outcomes of patients after ischemic stroke who underwent home rehabilitation. The RMA (Rivermead Motor Assessment) and ADL (activities of daily living) scales were used for evaluation. A total of 20 patients underwent a 4-week home rehabilitation program in Cracow. In the studied group, most patients showed functional improvement after the 4-week rehabilitation period. Predictive models were created (Net1, Net2, Net3) using artificial intelligence algorithms, including regression and classification methods. The analysis results indicate that the best outcomes in predicting the RMA and ADL indicators. For Net2, the prediction accuracy for the ADL indicator was 94.4%, which is significantly higher compared to the other indicators. The RMA1-3 indicators achieved relatively low accuracy rates of 38.9–44.4%. In contrast, for Net3, the RMA1-3 indicators showed high accuracy, achieving 89.1–91.3% correct results. The conclusions of the study suggest that using a combination of the Net2 and Net3 models can contribute to optimizing the rehabilitation process, allowing therapy to be tailored to the individual needs of patients. The research proves that it is possible to predict the effect of rehabilitation by using AI. The implementation of such solutions can increase the effectiveness of post-stroke rehabilitation, particularly through the personalization of therapy and dynamic monitoring of patient progress.

In the field of transport, and more precisely in supply chains, if any of the vehicle components are damaged, it may cause delays in the delivery of goods. Eliminating undesirable damage to the means of transport through the possibility of predicting technical conditions and a state of failure may increase the reliability of the entire supply chain. From the aspect of sustainability, the issue of reducing the number of failures also makes it possible to reduce supply chain disturbances, to reduce costs associated with delays, and to reduce the materials needed for the repair of the means of transport, since, in this case, the costs only relate to the replaced elements before their damage. Thus, it is impossible for more serious damage to occur. Often, failure of one item causes damage to others, which generates unnecessary costs and increases the amount of waste due to the number of damaged items. This article provides an author’s method of technical condition prediction; by applying the method, it would be possible to develop recommended maintenance activities for key elements related to the safety and reliability of transport. The combination of at least two artificial intelligence methods allows us to achieve very good prediction results thanks to the possibility of individual adjustments of weights between the methods used. Such predictive maintenance methods can be successfully used to ensure sustainable development in supply chains.

In this paper, a new model supporting decisions about product allocation in an order-picking shelf warehouse is presented. Industry 4.0 pays attention to inciting the processes, self-analysis and self-optimization of the short response time to market changes, and the maximum use of related data. Methods for solving the product allocation problem (PAP) are not enough to meet the requirements of Industry 4.0. The authors present a new approach for solving PAP. The novelty introduced in the model is based on correlated data—products parameters, clients’ orders and warehouse layout. The proposed model contains elements of intelligence. The model, after product classification and allocation, analyzes its effectiveness by a simulation of the order-picking process. The application of artificial neural networks (ANN) as a part of the computing model enables the analysis of large data sets in a short time. The presented study has proved the proposed model, both for practical and scientific purposes. Relying on the research results, the total warehouse cost could be reduced by 10 to 16 per cent. With the use of the proposed model, it is possible to predict the effect of future actions before their execution. The model can be implemented in most conventional warehouses to raise the throughput performance of the order-picking process.