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.

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.

This paper presents a compelling argument for optimizing the logistics flow of wiring harnesses within the automotive industry to address the rising production demands of vehicle manufacturers. It introduces an innovative assembly line structure specifically designed to boost efficiency and enhance responsiveness to client needs. Drawing from data gathered from an actual assembly line dedicated to producing engine harnesses for K9K engines, this study offers a practical and impactful foundation for its proposed optimization strategies. The new assembly structure effectively merges the benefits of a dynamic line—which emphasizes efficient space utilization and flexibility—with the strengths of a rotary line, particularly in light of the increasing complexity associated with harness production. The paper features a mathematical model that calculates cycle times for workstations within this new system architecture, optimizing the entire production process. Moreover, it illustrates how advanced modeling, simulation, and optimization techniques using WITNESS Horizon Version Release 25.0 can identify necessary adjustments for achieving optimal assembly line balance. Additionally, this research addresses pressing environmental concerns by proposing a robust recycling strategy for the scrap produced during wiring harness manufacturing. By advocating for sustainable practices and responsible waste management, the study highlights the importance of minimizing the ecological footprint of the automotive manufacturing process. In summary, this research provides essential insights and practical solutions for optimizing wiring harness logistics flow in the automotive industry. By implementing these strategies, manufacturers can significantly enhance their production capacity, improve operational efficiency, and maintain competitiveness in an ever-evolving market landscape.

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.

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.