This work aims to implement and analyze the effect of the Single Minute Exchange of Die (SMED) implementation in the bean packaging operation in a company in east Minas Gerais, Brazil. Design/Methodology/Approach: The research methodology used was action research. Two cycles of action research were conducted; the first to carry out phase one of SMED, and the second to execute phases two and three. Originality/Research gap: There are few studies on the application of Lean Manufacturing tools in agroindustry. Some works present case studies, mainly in the food supply chain aiming to fill this gap. Regarding SMED applied in agribusiness, no work was found. Key statistical results: The implementation of this methodology allowed the reduction of setup time by around 58%, the distance travelled by operators in the process by approximately 50%, in addition to gains in a production capacity of 14%. Practical Implications: It is concluded that the application of the methodology caused an increase in the company’s productivity, as it was possible to obtain gains in productive capacity without changing the amount of hours worked or the number of employees involved in the production process. Limitations of the investigation: This methodology was applied only once and the challenges encountered were not documented.

This paper presents a framework to analyze the problem of real-time management of Smart Grids. For this purpose, the energy management is integrated with the power system through a telecommunication system. The use of Multiagent Systems (MAS) leads the proposed algorithm to find the best-integrated solution, taking into consideration the operating scenario and the system characteristics. With this framework it was possible to evaluate the design of the energy management and the impact of the algorithm developed in the MAS. In the same way, the data sent from the power system to be used for energy management have a direct impact on his behavior. The proposed framework is tested with the help of a microgrid, so the results may be replicated.

This study investigates the optimization of tree detection in static images using YOLOv5, YOLOv8, and YOLOv11 models, leveraging a custom non-standard image bank created exclusively for this research. Objectives: To enhance tree detection in static images by comparing the performance of YOLOv5, YOLOv8, and YOLOv11 models. The comparison involved hyperparameter tuning and the application of various optimizers, aiming to improve model performance in terms of precision, recall, F1, and mean average precision (mAP). Design/Methodology/Approach: A custom image bank was utilized to train YOLOv5, YOLOv8, and YOLOv11 models. During training, the hyperparameters’ learning rate and momentum were tuned in combination with the optimizers SGD, Adam, and AdamW. Performance metrics, including precision, recall, F1, and mAP, were analyzed for each configuration. Key Results: The optimization process achieved precision values of 100% with Adam for YOLOv8 and SGD for YOLOv11, and recall of 91.5% with AdamW on YOLOv8. Additionally, mAP values reached 95.6% for AdamW on YOLOv8 and 95.2% for SGD on YOLOv11. Convergence times for mAP were also significantly reduced, demonstrating faster training and enhanced overall model performance. Originality/Research gap: This study addresses a gap in tree detection using YOLO models trained on non-standard image banks, a topic that is less commonly explored in the literature. The exclusive development of a custom image bank further adds novelty to the research. Practical Implications: The findings underscore the effectiveness of model optimization in tree detection tasks using custom datasets. This methodology could be extended to other applications requiring object detection in non-standard image banks. Limitations of the investigation: This study is limited to tree detection within a single custom dataset and does not evaluate the generalizability of these optimizations to other datasets or object detection tasks.

The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match.