EMC - Artigos publicados em periódicos

URI Permanente para esta coleçãohttp://200.137.215.59//handle/ri/1307

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Mean square relative displacements in a flat graphene monolayer
(2025) Rodrigues, Clóves Gonçalves; Calixto, Wesley Pacheco; Rabelo, José Nicodemos Teixeira
The mean square relative atomic displacements (MSRD) in a flat graphene monolayer are investigated in the approximation of weak anharmonicity. Numerical results, for not very high temperatures, are calculated using a parametric interatomic potential constructed specifically for graphene. In summary, our results show an overall increase in the MSRD as the interatomic distances increase, the MSRD in the direction of the straight line connecting two pairs of atoms are smaller than perpendicular ones, and in comparison with other twodimensional lattices, the MSRD in graphene lattice are greater than those in the hexagonal and square lattices.
Control and stabilization of quadcopters subjected to propeller failures
(2025) Bulhões, Júnio Santos; Martins, Cristiane Lopes; Pacheco, Viviane Margarida Gomes; Magalhães, Alana da Silva; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This work develops an auxiliary control system based on sliding mode control in order to stabilize quadcopters in the event of propeller failures, a significant challenge in the operation of unmanned aircraft. The proposed approach includes the implementation of modern control techniques, extensively tested in both a nonlinear simulator and a testing platform, allowing the reproduction of scenarios with up to 30% power loss in one of the motors. The combination of detailed simulations and practical experiments demonstrates the efficiency of sliding mode control, which is able to mitigate the effects of failures by reducing deviations in the 𝜙 and 𝜃 angles by more than 80% at the initial moments and maintaining partial stability of the angle 𝜓. In addition to surpassing other approaches in terms of efficiency, the proposed method preserves the aircraft’s autonomy, offering a robust and practical solution for application in real operational environments, ensuring greater safety and reliability in quadcopter control.
Multidimensional robustness analysis for optimizing complex systems
(2025) Paiva, João Ricardo Braga de; Pacheco, Viviane Margarida Gomes; Bulhões, Júnio Santos; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This work proposes the development of a metric for the analysis of operational robustness in systems, focusing on performance, complexity, and stability as key components. The methodology integrates these factors, enabling the assessment of the system’s ability to meet its design requirements, its internal dynamics and external interactions, and its capacity to return to equilibrium after disturbances. The metric is applied in three case studies: an intensive care unit, process scheduling in operating systems, and traction and braking in electric vehicles. The results show that, in scenarios with higher robustness, the contributions of performance, complexity and stability are balanced, with performance contributing around 30% and complexity and stability each contributing approximately 35%. In contrast, scenarios with lower robustness exhibit greater variation in the contributions of these components. These findings suggest that the proposed metric is an efficient tool for both quantitative and qualitative analyses, providing more detailed perspectives for decision making in complex systems.
Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms
(2025) Silva, Carlos Leandro Borges da; Pires, Thyago Gumeratto; Silva Filho, Antonio Marcelino da; Bulhões, Júnio Santos; Belo, Orlando Manuel Oliveira; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50 Hz and 60 Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities.
Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: pilot study
(2025) Nogueira, Solange Amorim; Luz, Fernanda Ambrogi Barbosa da; Camargo, Thiago Fellipe Ortiz de; Oliveira, Júlio César Silveira; Campos Neto, Guilherme de Carvalho; Carvalhaes, Felipe Brazão Farinha; Reis, Márcio Rodrigues da Cunha; Santos, Paulo Victor dos; Mendes, Giovanna de Souza; Loureiro, Rafael Maffei; Calixto, Wesley Pacheco
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images. The results demonstrate the efficiency of the proposed artificial intelligence model, with a Dice coefficient of 87% and an average Intersection over Union of 0.8, reflecting high agreement with the manual segmentations produced by experts and surpassing traditional interpretation methods. The internal and external validation of the model corroborates its future applicability in real clinical scenarios, offering a new perspective in the analysis of myocardial perfusion scintigraphy images. The integration of artificial intelligence into the process of analyzing myocardial perfusion scintigraphy images represents a significant advancement in diagnostic accuracy, promoting substantial improvements in the interpretation of medical images, and establishing a foundation for future research and clinical applications, such as artifact correction.
Hybrid machine learning model for disinfectant dosing in small-scale water treatment under data scarcity
(2025) Sato, Diego Takashi; Belo, Orlando Manuel Oliveira; Castro Junior, Antonio Pires de; Pacheco, Viviane Margarida Gomes; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
Disinfection by-products, including trihalomethanes and haloacetic acids, pose persistent risks to human health and aquatic ecosystems, particularly in small-scale water treatment plants characterized by limited automation and incomplete monitoring records. This study proposes a hybrid model that integrates extreme gradient enhancement with seasonal trend decomposition, allowing incomplete time series to be partitioned into trend and seasonal components, thereby improving prediction stability and improving interpretability of variable influence. The main contribution is a method that explicitly addresses seasonal variability and data scarcity while preserving predictive accuracy under infrastructure constraints, achieving 𝑅2 ≥ 0.90 and RMSE values between 0.15 and 0.30. The model was validated in a real decentralized system, where it exhibited high performance even with data missing up to 30%, producing monthly reductions of approximately 450 g of trihalomethanes and 800 g of haloacetic acids, along with lower chlorine and fluoride consumption. By integrating technical, environmental, and economic dimensions, including measurable financial returns with a positive annual ROI and a short payback period, the approach provides a replicable solution for dosing control in data-limited contexts, aligned with the Sustainable Development Goal 6 of the United Nations and the advancement of responsible digital strategies in the water sector.
Connection-based framework for assessing natural complexity in nonlinear adaptive systems
(2025) Pacheco, Viviane Margarida Gomes; Wainer, Gabriel Andrés; Gomes, Flávio Adalberto; Martins, Weber; Paiva, João Ricardo Braga de; Martins, Marcella Scoczynski Ribeiro; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
This study introduces a quantitative framework for assessing natural complexity in adaptive systems, based on connection measures weighted by sensitivity indices. The methodology integrates system modeling, sensitivity analysis, and complexity assessment, enabling continuous monitoring and decision support in dynamic environments. Natural complexity is defined as an optimal level at which the system behaves in accordance with its nature, sustaining coherence between structure and function. By employing sensitivity-weighted connections, the framework captures both internal organization and adaptive dynamics, overcoming limitations of traditional metrics such as Shannon entropy and fractal dimension, which often neglect interaction intensity and temporal variability. The framework is validated through two case studies: a computational model of an Intensive Care Unit and a real-world startup acceleration ecosystem. In the Intensive Care Unit, periods of overload were identified through peaks in complexity, associated with an increased number of highly sensitive parameter connections. In contrast, in the startup ecosystem, systemic idleness was reflected by lower complexity levels, driven by weakly influential interactions among actors. These findings highlight the responsiveness and interpretability of the proposed metric compared to conventional approaches, particularly in tracking adaptive states over time. This connection-based framework supports the management of adaptive information systems, offering a dynamic and scalable complexity assessment tool. Its applicability spans medical informatics, business management, and distributed systems optimization, providing real-time insights that improve resilience and efficiency. In addition, the approach aligns with industry 4.0 paradigms, facilitating preventive analyses and adaptive decision-making in advanced technological environments. By offering a unified methodology for complexity evaluation, this research advances understanding and control of complex adaptive systems.
Artificial intelligence-driven protocol for secure and standardized maneuver control in electrical substations
(2025) Campos, Gustavo Havilá de Freitas; Pacheco, Viviane Margarida Gomes; Reis, Márcio Rodrigues da Cunha; Rodrigues, Clóves Gonçalves; Silva, Saulo Rodrigues e; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
Notwithstanding recent advances in substation automation, no existing protocol integrates human–machine interaction, intelligent interlocking, operational autonomy, and artificial intelligence analysis in sequential maneuvering contexts. This study proposes an automated interface to optimize and control switching operations in electrical substations by integrating operational protocols, automated documentation generation, and artificial intelligence techniques with interactive graphical visualization. The developed solution enables sequential command execution, classification of operational events, and automatic generation of auditable reports, enhancing accuracy and traceability in operations. A total of 108 real files, corresponding to 54 events with documented failures, were analyzed and used to train and validate a recurrent convolutional neural network model. The system achieved an accuracy of 82.92% in error detection, along with reductions of 42.7% in the average operational response time and 38.5% in failure frequency. In addition to standardizing procedures, the interface demonstrated adaptability to different substation topologies and configurations, establishing itself as a scalable, secure, and efficient alternative for assisted operation environments. The results suggest that the proposed solution contributes to reducing inconsistencies, increasing decision-making autonomy, and strengthening operational safety in the power sector.
A joint channel modeling, parameter estimation and geometry-based indoor localization for 5g systems
(2026) Conceição, Paulo Francisco da; Lemos, Rodrigo Pinto; Rocha, Flávio Geraldo Coelho
This work proposes a Mobile Station (MS) localization method for indoor environments using a single Base Station (BS) equipped with a massive Multiple-Input Multiple-Output (mMIMO) antenna array. The proposal can be divided into three main stages: (1) channel modeling, (2) estimation of localization parameters, and (3) estimation of MS position. We consider a millimeter-Wave (mmWave) mMIMO channel model to estimate five localization parameters: Time of Arrival (ToA), two-dimensional Angle of Departure (2D-AoD)-azimuth and elevation, and two-dimensional Angle of Arrival (2D-AoA)-azimuth and elevation. Then, from AoA and AoD data, the proposed method can analyze the various transmission paths and identify whether there is a Line-of-Sight (LoS) path, allowing the automatic determination and application of the most suitable localization algorithm. The system model is comprehensive, approaching a 5G small cell with mmWave and mMIMO technologies transmitting in an indoor environment with LoS and Non-Line-of-Sight (NLoS) conditions and multiple Scatterers. Simulations are carried out, and the results are compared to those of three related methods present in the literature. The obtained results demonstrate that the proposed method achieves sub-metric accuracy under LoS conditions and outperforms related methods in NLoS conditions. Additionally, the proposed method is simpler, faster, and relies on a single BS for localization.
Challenges of digital transformation and operations management on small businesses in Araguari
(2025) Silva, Mayra Camila Jorge; Chaves, Stephanie Aparecida Campos; Dutra, Michael David de Souza
Digital transformation and operations management have proven to be a powerful driving force for competitiveness and business growth. In this context, micro and small enterprises (MSEs) need to adopt digital technologies and implement efficient management control practices in order to face the challenges of today’s market with more assertive decision-making. However, limited research has explored the combined impact of these factors on MSEs in emerging economies. To address this gap, this article examines the effects of digital transformation and management controls on fifteen MSEs from various segments within the commerce, industry, and service sectors. All the companies are located in Araguari, Minas Gerais, Brazil, and participate in the “Brasil Mais” program. This research is descriptive and exploratory, utilizing both qualitative and quantitative analyses. Data collection focuses on MSEs’ productivity and maturity, employing unstructured interviews and the Innovation Radar questionnaire as key instruments. The results revealed that companies face major challenges in implementing digital transformation and management controls. It also indicates the importance of proper planning, employee training, and the search for solutions adapted to the needs of MSEs. In conclusion, this study highlights the importance of digital transformation and management practices for the success of MSEs in Araguari. These practices can boost the innovation, productivity, and competitiveness of these companies.
Comparative analysis of weighting-factor-free predictive control strategies for direct torque control in permanent magnet synchronous machines
(2025) Bonaldo, Jakson Paulo; Zerdali, Jacopo Riccio Emrah; Monteiro, Raul Vitor Arantes; Wheeler, Patrick
Direct torque control (DTC) based on the finite control set model predictive control (FCS-MPC) provides a straightforward and intuitive solution for controlling permanent magnet synchronous motors (PMSMs). However, conventional FCS-MPC relies on appropriately tuned weighting factors in the cost function, which have a significant impact on the control performance and increase design complexity. This paper presents a comprehensive experimental comparison of emerging FCS-MPC strategies for DTC of PMSMs that eliminate the need for weighting factors. Specifically, a sequential FCS-MPC approach is benchmarked against decision-making-based FCS-MPC methods that employ Euclidean distance normalisation. Extensive experimental results, obtained across a wide range of operating conditions, are used to assess current total harmonic distortion (THD), torque and flux ripple, and transient performance. Results indicate that while all methods yield comparable current THD, decision-making-based strategies achieve superior torque and flux regulation with reduced ripple compared to the sequential approach. These findings demonstrate that decision-making-based FCS-MPC methods provide additional flexibility in defining control objectives, eliminating the need to design weighting factors, such as those used in the sequential method while offering superior performance.
Simulação numérica da condução e convecção em transformadores imersos em ar e óleo via OpenFOAM
(2025) Viana, Joyce Tamires de Souza; Maionchi, Daniela de Oliveira; Monteiro, Raul Vitor Arantes; Fonseca, André Luiz Amorim da
The article addresses the growing global demand for electrical energy, driven by technological advancements, which requires robust energy infrastructures. The research focuses on the thermal analysis of this equipment, which is essential to guarantee efficiency and safety.Transformers, composed of silicon-steel cores and copper coils, utilize cooling systems to dissipate the heat generated by the Joule effect.The study uses computational simulations with air and oil cooling, employing the chtMultiRegionFoam solver from the OpenFOAM (version 12) software along with the heatedDuct tutorial, applying the FiniteVolume Method to investigate the thermal efficiency of air-cooled (ANAN) and oil-cooled (ONAN) transformers.The simulations consider the geometry, the properties of air and mineral oil, and the turbulence model 𝑘 − 𝜀.The results showed that the air-cooled transformer exhibits slower heat dissipation, while the oil-cooled transformer provides more efficient heat exchange, with more uniform temperatures, although with greater heat retention in some regions.The study concludes that the choice of the cooling fluid directly impacts the thermal behavior, being essential for optimizing the performance and reliability of electrical systems.
Impacto da temperatura ambiente no resfriamento de transformadores elétricos: uma investigação usando cfd para regiões do estado do Espírito Santo
(2026) Feroni, Rita de Cassia; Viana, Joyce Tamires de Souza; Maionchi, Daniela de Oliveira; Monteiro, Raul Vitor Arantes; Fonseca, André Luiz Amorim da; Feroni, Wilson José; Silva, Junior Gonçalves da
Electrical transformers are being studied toincrease energy efficiency and reduce environmental impact. In this context, the objective of the present work is to evaluate, using computational fluid dynamics (CFD), the impact of ambient temperature on the cooling of electrical transformers for different temperature values, covering the climatic conditions of regions in the state of Espírito Santo, Brazil. A 250 kVA electrical transformer was studied using CFD, with the air temperatures of the municipalities of interest applied as boundary conditions in the modeling. The results show that Februaryis historically the hottest month, with the highest average maximum temperature in Alegre at 34.2 °C, and peak temperatures were recorded in Marilândia (42.1 °C) and Ecoporanga (40.0 °C). From the computational simulation, it was observed that temperatureis distributed from its maximum values near the surfaces of the coils and the core to its minimum values at the external surfaces of the transformer. The average and maximum oil temperatures increase as the ambient temperature rises, due to the decrease in heat dissipation to the outside. This result suggests that greater attention should be given to devices installed in the municipalities of Alegre, Marilândia, and Ecoporanga.
Active damping of electromagnetic torque oscillations in synchronous generators using rotor excitation control
(2025) Gomes, Luciano Coutinho; Reis, Márcio Rodrigues da Cunha; Pacheco, Viviane Margarida Gomes; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco; Aleluia Junior, Leovir Cardoso; Alves, Aylton José
This study proposes an active method for mitigating electromagnetic torque oscillations in salient-pole synchronous generators by injecting controlled sinusoidal components into the rotor excitation. Unlike conventional approaches that rely on damper windings or structural modifications, this strategy reduces oscillations without requiring additional mechanical components, utilizing only the generator’s excitation system. The methodology includes mathematical modeling, computational simulations, and spectral analysis of electromagnetic torque oscillations under different operating conditions. The results indicate that the proposed technique effectively reduces oscillations, achieving attenuation between 35% and 85%, depending on the applied configuration. Furthermore, its application extends to industrial synchronous motors, contributing to the reduction of mechanical resonances and enhancing the reliability of electrical systems. The proposed strategy does not require structural modifications, making it a cost-effective and viable alternative to improve the stability, performance, and efficiency of thermal, hydroelectric, and renewable energy generation systems, as well as their integration into smart grids subject to harmonic distortions.
Measurement of grounding resistance over a seasonal cycle with hygroscopic materials in the encapsulation of grounding rods
(2025) Silva Filho, Antonio Marcelino; Rodrigues, Clóves Gonçalves; Belo, Orlando Manuel Oliveira; Coimbra, Antonio Paulo; Pacheco, Viviane Margarida Gomes; Calixto, Wesley Pacheco
This paper evaluates the feasibility of using hygroscopic lightweight structural concrete (LSC) materials in electrical grounding systems to reduce grounding resistance and apparent soil resistivity across seasonal variations. Laboratory and field experiments monitored the grounding resistance of rods encapsulated with LSC materials, composed of vermiculite, clay, sawdust, and cement, compared to unencapsulated systems. The results indicate an average reduction of to 40% in grounding resistance and 20% in apparent soil resistivity for encapsulated systems, even in soils with resistivity up to . Encapsulated rods demonstrated superior performance in single-rod and three-rod inline systems, commonly used in practical applications, with seasonal variations controlled within 15%. This study highlights the application of LSC materials, traditionally used in civil engineering, as grounding enhancement materials, offering improved seasonal stability and efficiency in high-resistivity soils, while presenting a technically and economically viable alternative to enhance the reliability of electrical grounding systems.
Interpretable artificial intelligence modeling of pre-emergence herbicide bioactivity in weakly weathered soils for optimized dose recommendations, part I: diclosulam
(2025) Almeida, Danielle Resende; Souza, Virgínia Dami Ademir Xavier de; Alves, Márcio Aliomar; Souza Júnior, Valdomiro Severino de; Coimbra, Antonio Paulo; Rodrigues, Clóves Gonçalves; Calixto, Wesley Pacheco
Conventional herbicide recommendations seldom consider soil physicochemical attributes beyond texture, overlooking key factors that govern bioavailability and environmental fate. This study presents an integrated framework for optimizing the doses of pre-emergence herbicides in eight highly heterogeneous, weakly weathered soils derived from Andean sediments. Bioassays with Sorghum bicolor were modeled using extreme gradient enhancement, interpreted using Shapley additive explanations, and refined using symbolic parametric optimization. The approach replaces soil-specific dose–response curves with a single generalizable function calibrated on continuous soil descriptors. A three-dimensional response surface relating herbicide dose and soil enabled intuitive visualization of non-linear interactions and supported the derivation of an optimized and interpretable dose–response function for herbicide recommendation. Soil , electrical conductivity, sulfate, and magnesium emerged as the main predictors of diclosulam bioactivity. Although the dose required for 90% weed control varied between soils, optimized rates remained below active ingredient ha−1, representing reductions greater than 70% relative to commercial label recommendations. The final model, interpretable and parsimonious, predicts weed control as a function of herbicide dose and soil , with flexibility to incorporate additional variables. These results highlight the potential for soil-adaptive herbicide management by identifying site-specific attributes important for optimizing applications, thus improving efficiency, reducing environmental contamination risk, and advancing sustainable and precision agriculture in alignment with the United Nations 2030 Agenda for Sustainable Development.
Hybrid evolutionary meta-optimization assisted by recurrent neural networks for interpretable parametric symbolic models
(2025) Gomes, Flavio Adalberto; Araújo, Wanderson Rainer Hilário de; Reis, Márcio Rodrigues da Cunha; Furriel, Geovanne Pereira; Ribeiro, Guilherme Alberto Sousa; Pacheco, Viviane Margarida Gomes; Martins, Marcella Scoczynski Ribeiro; Rodrigues, Clóves Gonçalves; Coimbra, Antonio Paulo; Calixto, Wesley Pacheco
The modeling of complex nonlinear systems poses persistent challenges for evolutionary optimization due to high-dimensional search spaces, fragile convergence behavior, and the difficulty of balancing exploration and exploitation under computational constraints. To address these limitations, this study investigated a hybrid evolutionary meta-optimization strategy assisted by recurrent neural networks, designed to guide and stabilize the search process during the identification of analytical models. The proposed framework integrates symbolic structure discovery with continuous parametric refinement, enabling the simultaneous optimization of coefficients and real-valued exponents within an evolutionary setting. A recurrent neural network was embedded in the meta-optimization loop to adaptively influence the evolutionary dynamics, improving convergence stability and search efficiency while preserving structural diversity. Comprehensive search trajectories were recorded and subsequently analyzed using Shapley Additive Explanations, allowing the association of optimized parameters with dominant physical mechanisms. The method was evaluated on synthetic benchmarks, electromechanical systems, thermodynamic maps, and physicochemical surfaces, consistently preserving global morphological features and structural coherence. In all cases, stable generalization behavior was observed, with normalized relative errors typically of the order of and structural similarity indices exceeding 0.85, along with a statistically consistent separation from reference methods. The results demonstrate that neural-assisted evolutionary meta-optimization constitutes a viable strategy for improving convergence robustness and analytical model discovery in complex system identification problems.
Induction motor fault diagnosis based on the machine temperature, vibration analysis and sensors fusion
(2025) Sifuentes Filho, Daniel Pedrosa; Ginu, Ygor Ferreira; Andrade Junior, Khristian Marques de; Alvarenga, Bernardo Pinheiro de; Paula, Geyverson Teixeira de
The most common motor used for industrial, residential and commercial applications is the induction motor (three or single phase). This motor is very reliable, but faults still may occur. The present paper focuses on the diagnosis of induction motor faults based on its temperature and vibration behaviors on steady-state operation. The proposed method is based on the Extended Park Transform, enabling sensor fusion which reduces the amount of data required for fault identification to 1/3 and allows the usage of a shallow artificial neural network. To validate the proposed method, experiments have been carried using a single phase induction motor operating under normal and fault conditions (short-circuit between main winding turns, auxiliary turns, main-auxiliary windings and with contaminated bearing lubrication). The results proves the efficacy of the proposed method, which has reached an accuracy over 99.5% in the process of fault identification using low cost sensors/equipment.
Where should the toilets be in the factory? A case study in Brazil
(2025) Dutra, Michael David de Souza
Improved sanitation has a significant benefit for both public health and the economy. Sanitation for all is a component of the 2030 Agenda for Sustainable Development. However, the literature reveals that some workplaces reprimand employees for using washrooms outside of formal, scheduled breaks due to the perceived negative impact on productivity. Furthermore, research shows that women often limit their washroom visits in workplaces where the facilities are far from their workstations, influenced by concerns about reprimand. While placing washrooms inside production areas is sometimes discouraged for certain plants based on the products they manufacture, managers may remain resistant to the idea even when no such restrictions apply. As a result, a conflict arises between employee health and productivity. This paper aims to mitigate this conflict and contribute to the literature by presenting, for the first time, a case in which locating washrooms within the production facility, detached from its perimeter walls, and near the department with the highest employee density can be economically beneficial. The case studied involved designing a new plant for a Brazilian garment manufacturer via Systematic Layout Planning. Layout alternatives were evaluated using Discrete-Event Simulation and a Net Present Value analysis. Results showed that having washrooms inside the production area becomes economically viable when the profit per unit surpasses $7.81 in the case studied. This case suggests that managers should consider placing washrooms within the production area during layout planning, thereby challenging the hegemonic discourse that restrooms should never be inside a production facility.
Optimizing photovoltaic generation placement and sizing using evolutionary strategies under spatial constraints
(2025) Silva, Carlos Henrique dos Santos; Mendes, Saymon Fonseca Santos; Garces Negrete, Lina Paola; Lopez Lezama, Jesus Maria; Muñoz Galeano, Nicolás
This study presents a methodology for optimizing the placement and sizing of photovoltaic generation in power distribution networks. In addition to technical and budgetary constraints, the proposed approach incorporates georeferenced spatial restrictions to determine the optimal location and capacity of the generation units. These spatial constraints are not commonly considered in similar studies, which make them the main contribution in the proposed methodology. The proposed approach is divided into three stages and utilizes simulations in OpenDSS and QGIS, which employ optimization strategies such as the Hybrid Evolutionary Strategy and the Hybrid Genetic Algorithm. The methodology was evaluated on the IEEE 34-bus system and a real feeder. The results demonstrate the effectiveness of the proposed approach, which achieves significant reductions in system losses—14.48% for the IEEE 34-bus system and 14.08% for the real feeder—while also improving voltage profiles. These findings validate its applicability in the efficient and sustainable planning of power distribution systems.

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