This paper proposes a methodological framework for linking standardized carbon footprint reporting with structured decision support in logistics. The approach integrates the GHG Protocol framework and the ISO 14064 standard in order to formalize emissions inventories, reporting requirements, and verification constraints into a coherent, transparent, and auditable analytical structure. While existing standards provide robust guidance for the quantification and reporting of greenhouse gas emissions, their systematic integration into decision support representations remains limited. The main contribution of the paper consists of the formal operationalization of carbon accounting processes into decision variables, constraints, and performance indicators that preserve traceability, transparency, and compatibility with external verification requirements. A simplified linear programming formulation is employed as a standard-driven decision support abstraction, illustrating how emissions-related data derived from standardized reporting can be consistently translated into operational constraints and analytical indicators. The mathematical formulation is not intended to replace detailed logistics optimization models, but to demonstrate the methodological linkage between emissions reporting, verification requirements, and structured decision-oriented analysis. The proposed framework is illustrated through a logistics hub case study using average emission factors and estimated consumption data. The numerical results serve an illustrative purpose and highlight the functioning of the framework, rather than providing fully calibrated operational solutions. The methodology is designed to be reproducible and auditable and may be extended to other industrial sectors, as well as to more advanced modeling settings incorporating dynamic or stochastic elements.

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics.

Lately, the logistics sector has seen accelerated development, which has led to general economic growth, but, at the same time, it has caused considerable environmental damage due to the excessive consumption and emissions that are currently affecting society at large. Since logistics activities are considered some of the most polluting economic activities, this present article aims to present the advantages of implementing the green logistics concept. To this purpose, the activity of a logistics centre in Romania was analysed, with a focus on the greenhouse gases (GHGs) produced as a consequence of this economic activity, and its carbon footprint was calculated according to the GHG Protocol. Although this global standard is based on an integrated approach to how GHG emissions are calculated, there is limited evidence about its degree of implementation by companies. The results of the analysis revealed that the consumption of energy and fuel by the logistics sector has a significant impact on the environment. This impact is maintained, albeit at a smaller scale, even if the technology is replaced and the equipment used by companies to carry out their activities is increasingly performant.

Considering the strong need for improvement of security and multimedia systems in the automotive industry, wiring harness production is becoming more and more important. Adapting to the integration of new technologies on the vehicle is a challenge for wiring harnesses manufacturers. Now the production of wiring harnesses is still quite dependent on human resources, the way of distributing the workload on the workstations having a large share in increasing productivity. Components such as terminals, connectors and seals are getting smaller and smaller, making the manual handling more difficult. A solution to this problem could be increasing the automation degree in wiring harness production. The objective of the research topic approached in this article is to identify solutions for optimizing the wiring assembly flow by partially automating the production flow. The concern for the application of automated processes in the production of car wiring is not new, but so far, the wiring manufacturers have been more focused on automating the prefabrication of the elements that compose the harness and not the wiring harness assembly line. From an economic point of view, the automation of the assembly flow would increase productivity by reducing fabrication time and the uncertainty given by the human resource dependence.

Industrial development has implicitly led to the development of new systems that increase the ability to provide services and products in real time. Autonomous mobile robots are considered some of the most important tools that can help both industry and society. These robots offer a certain autonomy that makes them indispensable in industrial activities. However, some elements of these robots are not yet very well outlined, such as their construction, their lifetime and energy consumption, and the environmental impact of their activity. Within the context of European regulations (here, we focus on the Green Deal and the growth in greenhouse gas emissions), any industrial activity must be analyzed and optimized so that it is efficient and does not significantly impact the environment. The added value of this paper is its examination of the activities carried out by mobile robots and the impact of their electronic components on the environment. The proposed analysis employs, as a central point, an analysis of mobile robots from the point of view of their electronic components and the impact of their activity on the environment in terms of energy consumption, as evaluated by calculating the emission of greenhouse gases (GHGs). The way in which the activity of a robot impacts the environment was established throughout the economic flow, as well as by providing possible methods of reducing this impact by optimizing the robot’s activity. The environmental impact of a mobile robot, in regard to its electronic components, will also be analyzed when the period of operation is completed.

This paper presents a logistic flow of assembling automotive rear axles. The product is presented in detail starting from the detailed research and analysis of relevant documentation about its functionality, including the manufacturing logistic flow diagram and the required equipment for the product manufacturing and assembly. This study is focused on optimizing the logistic flow for the manufacturing and assembly of automotive rear axles using WITNESS Horizon for system modeling and simulation in order to conduct system diagnostics, identify problems, and find solutions that will facilitate the optimization process. The study included a comprehensive assessment of the logistic flow, highlighting the performance of the equipment involved and identifying potential bottlenecks. Using the results obtained after the simulations, the Simplex linear mathematical method was applied to maximize production efficiency and profitability, considering the suppliers’ capacity constraints and the components’ delivery requirements. The results demonstrated a significantly optimized rear-axle production process, with increased profitability and improved productivity by eliminating identified bottlenecks. This research contributes to a deeper understanding of the complexities within the automotive industry and provides a solid foundation for continuously improving manufacturing and assembly processes.

The paper presents a set of deep learning algorithms for detecting vibration anomalies in bearings using multivariate time series on datasets provided by Case Western Reserve University. The study considers a problem of multiclassification of the condition of the bearings depending on the type of defect, but also on the degree of defect, considering only punctual defects in an incipient phase. Once the data sets are correctly labeled and the algorithms are trained on this data, they can accurately predict the type and the size of defect. The model with the best results in the set is RNN - CNN (Recurrent Neural Network with Convolutions) giving an accuracy greater than 97% in all (load) cases.

Industrial logistics is a very dynamic field, which involves moving equipment, flexibility, but also precise timing and accurate actions. Monitoring and control systems based on ZigBee can be implemented without causing obstructions or delays in the logistics flow. This paper presents different experiments using the industrial ZigBee equipment (coordinator, router, slave, bridge, sniffer) on different topologies and configurations. The purpose of these experiments is to find optimal topologies and settings for the ZigBee equipment, so that the implemented applications will be able to deal with the industrial environment, in order to acquire and transmit data as accurately as possible without losses of information and without interfering with the logistics flow. These experiments should be considered before implementing a ZigBee application in an industrial environment.

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.

The present paper aims to highlight the way in which the speeds and accelerations for the numerical control (NC) axes of the systems specific to a didactic stand with Automated Storage & Retrieval System (AS-RS) and guided transport system can be determined experimentally. The stand consists of an AS-RS system where the storage of pallets with products is achieved using 3 storage structures arranged circularly around the transport-transfer system. The pallets reach the entryexit station by means of a two-station guided transport system. Its role is to supply the AS-RS with pallets for storage and to evacuate the pallets from it. Stepper motors were used to drive the NC axes specific to the mentioned systems, and the control was realized by means of programmable logical controllers (PLC). The control programs were made in applications according to these PLC. A Supervisory control and data acquisition (SCADA) type interface was created and thus it was possible to determine experimentally the speeds and accelerations specific to the NC axes of the mentioned systems.

The paper presents the works performed by the authors in the field of AS/RS systems design and experimental small scale AS/RS physical model set-up for educational purposes. First part of the paper presents the virtual prototype of the 3D CAD model of the AS/RS system and the developing of the physical model set-up for an experimental small scale AS/RS for educational purposes. The specific hardware for control the AS/RS system and software for programming it are also included in a second part of the paper.

The paper presents the works performed by the authors in the field of AS/RS systems design and experimental small scale AS/RS physical model set-up for educational purposes. First part of the paper presents the virtual prototype of the 3D CAD model of the AS/RS system and the developing of the physical model set-up of an experimental small scale AS/RS for educational purposes. The specific hardware for control the AS/RS system and software for programming it are also included in a second part of the paper.

Present work illustrates author's contributions on structural and functional analysis of numerical controlled (NC) axes of electric driving system industrial robots (IR). A complex assisted study on NC axes' optimum structure of a gantry robot for part handling application and both IR's NC axes specific performance parameters and their influence on robot's overall performance has been performed. This article presents this study's 3(rd) Stage. The research was aimed at identifying the optimal structure of an IRB's NC axis, this time modifying the mechanical structure and keeping the electrical structure proposed in the 2(nd) Stage, in order to improve NC's axes / IR's performance level.