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Introduction/purpose: Cutting is a typical material process. However, an appropriate solution for simultaneous optimization of material machinability and tool life in material cutting processes has not been obtained yet. In this article, probabilistic multi-objective robust design (PMORD) is expounded and the robust design problem in the simultaneous optimization of material machinability and tool life is analyzed by taking the machining of ferrite-bainite dual-phase steel as an example. Methods: According to PMORD, the arithmetic mean and its deviation of various performance and utility indexes of alternative schemes are evaluated as twin independent responses, which respectively contribute a part of the partial preferable probability to the performance indexes. In the evaluation, the arithmetic average of the utility index is taken as the representative of the utility attribute, and the evaluation is made in accordance with the function or preference of the utility index. However, the deviation term is generally used as an unbeneficial type of the index (that is, the smaller the better) to participate in the evaluation. The product of the two parts of partial preferable probability is the actual partial preferable probability of the corresponding performance index. The product of the partial preferable probability of all performance utility indexes gives the total preferable probability of the corresponding scheme, which is the only index for each scheme to participate in the competition in robust design. Results: The optimization result of this example is that the tool life is 1297.3333 s (standard deviation is 2.0817 s) and the surface roughness is 2.22μm (standard deviation is 0.2μm), while the corresponding working conditions are that the heat treatment temperature of the material is 790°C, the cutting speed is 150m/min, the feed speed is 0.15mm/rev, and the cutting depth is 0.2mm. Conclusion: The example of the parameter optimization of cutting of ferrite-bainite dual-phase steel by means of PMORD indicates the rationality of the appropriate solution. [ABSTRACT FROM AUTHOR]

La asesoría curricular es un ejercicio mediante el cual un tutor calificado orienta a un estudiante en la selección de las asignaturas que cursará en algún periodo lectivo. Este ejercicio puede ser un desafío cuando se consideran las necesidades y preferencias de cada estudiante, por lo que en este trabajo se propone el diseño de un sistema de recomendaciones como apoyo a la asesoría curricular basado en la solución de un problema de optimización multiobjetivo. Los resultados de los experimentos mostraron que el sistema propuesto responde adecuadamente a las necesidades de estudiantes de diferentes programas educativos de la Universidad del Caribe, por lo que representa una opción pertinente para apoyar el trabajo de sus tutores académicos.

Copyright of Ingeniería (0121-750X) is the property of Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work developed a hybrid optimization algorithm called Combinatorial Optimization with a Coordinate Search Method, which allows for finding the optimal locations and capacities of distributed generation plants in an electrical distribution network. The algorithm was validated in the IEEE 69-bus test system, compared with the Elephant Herding Optimization method. Three scenarios were defined that were executed on the test system. In the first scenario, up to four distributed generation plants were located, reducing the value of the objective function to 0.0177. In the second scenario, satisfactory solutions were found that satisfied critical voltage and current restraints in the electrical system. All stresses were between 1.00 and 1.05 per unit for the first case. In the second case, two lines had the maximum current to flow through them limited to 1.00 ampere. In the third scenario, the algorithm was executed in the presence of capacitor banks. In this case, satisfactory results were also obtained, with reagent consumption in one of the distributed generation plants. [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products [ABSTRACT FROM AUTHOR]

Copyright of Ciencia e Ingenieria Neogranadina is the property of Ciencia e Ingenieria Neogranadina and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introducción: En este documento se presenta una metodología multiobjetivo aplicada al problema del Planeamiento de la Expansión de la Transmisión (PET) cuando se consideran las incertidumbres en la demanda y la generación eólica. Objetivo: Obtener planes de expansión robustos que minimicen los costos de inversión y maximicen el uso del recurso eólico, teniendo en cuenta su incertidumbre y la introducida por la demanda. Metodología: La metodología propuesta se basa en la Metodología de Escenario Reducido para representar estas incertidumbres. En la formulación de la metodología se consideraron: el modelo de red en DC, los planes de expansión que minimizan la inversión, la reducción en la carga y la generación eólica. Para obtener el algoritmo multiobjetivo, utilizado para minimizar los costos de expansión y la reducción de la energía eólica, se implementó un NSGA-II mejorado y un conjunto de planes óptimos de expansión de Pareto. Resultados: Se presenta el desempeño de los planes de expansión, los cuales fueron evaluados y comparados con trabajos anteriores para demostrar la solidez del enfoque propuesto. Todas las pruebas se realizaron en los sistemas Garver e IEEE de 24 nodos. Conclusiones: Al observar el número de veces que el plan de expansión lleva a cero el corte de carga y la energía eólica desperdiciada, con respecto a un valor establecido en este trabajo, se tiene que la metodología propuesta presenta un índice de rendimiento superior al 75,16% para el sistema Garver y al 98,97% para el sistema IEEE de 24 nodos.

Introducción: El diseño de cimentaciones sobre suelo tratado con cal se estudia como un tema importante en ingeniería geotécnica y ambiental. Con la aparición y el uso de algoritmos, es posible resolver problemas de optimización en ingeniería, lo que lleva, por ejemplo, a la disminución de cantidades de materiales, tiempo, energía y trabajo. Objetivo: Esta investigación tiene como objetivo optimizar la emisión de CO2 y el costo de la construcción de zapatas sobre un suelo tratado con cal hidratada utilizando el algoritmo recocido modificado (MSAA). Metodología: Se calcularon los parámetros de resistencia al corte (cohesión y ángulo de fricción) de un suelo limoso de la formación geológica Guabirotuba de Curitiba (Brasil) estabilizada con diferentes contenidos de cal (3, 5, 7 y 9%) a diferentes tiempos de curado (30, 90, y 180 días). Luego, con estos parámetros, la geometría de las zapatas se optimizó con MSAA minimizando el costo y las emisiones de CO2 de su construcción. La capacidad de carga final del suelo, los asentamientos producidos por la carga de servicio y el factor de seguridad de base fueron usados como restricciones de diseño. Resultados: Los resultados muestran que la mayoría de los problemas convergen a la misma solución para los costos y las emisiones de CO2 sin depender del tiempo de curado y del contenido de cal utilizado, debido a que las soluciones están restringidas principalmente por los asentamientos máximos permitidos. Conclusiones: Con el aumento del contenido de cal, la cohesión de las mezclas aumentó para todos los tiempos de curado estudiados y el ángulo de fricción no tuvo variaciones importantes en relación con la cantidad de cal administrada o con el tiempo de curado empleado. Los costos y la emisión de dióxido de carbono para la construcción de zapatas convergentes coinciden con los mismos resultados. En este sentido, se puede evitar el 9% de cal, y pequeños porcentajes de cal (es decir, 3-5%) se destinan a la mejora del suelo y reducen los costos de este procedimiento. Por otro lado, MSAA puede ser considerado como un algoritmo robusto debido a que ha logrado resultados casi iguales y, en algunos casos, mejores resultados en comparación con otros algoritmos para resolver problemas reportados en la literatura.

This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning problem considering user preferences, which in turn are proposed to be solved using a multiobjective evolutionary approach, in order to minimize energy consumption and maximize quality of service offered to users. Promising results are reported on realistic instances of the problem, compared with situations where no intelligent energy planning are used (i.e., 'Business as Usual' model) and also with a greedy algorithm developed in the framework of the reference project. The proposed evolutionary approach was able to improve up to 29.0% in energy utilization and up to 65,3% in user preferences over the reference methods. [ABSTRACT FROM AUTHOR]

La optimización multiobjetivo juega un papel fundamental en la ingeniería moderna, ya que permite diseñar sistemas y procesos donde se mejoran varios criterios simultáneamente. En este campo, se destaca el uso del llamado enfoque a posteriori, basado en la obtención de las soluciones no dominadas (conocidas también como el conjunto de Pareto) para, posteriormente, seleccionar de ellas la alternativa más conveniente para las condiciones reales. Éste ha mostrado ser la opción más efectiva, ya que evita el suministro previo de cualquier indicación de preferencia con respecto a los objetivos. Este trabajo presenta la implementación de una biblioteca de clases para la optimización multiobjetivo a través del método de entropía cruzada. Ésta se basa en una biblioteca previa, de MATLAB, desarrollada y publicada por los autores. La biblioteca fue implementada en C++, evitando el uso de otras bibliotecas aparte de la estándar, para garantiza su portabilidad y su capacidad de integración. Será distribuida bajo la Licencia Pública General Reducida de GNU (versión 3). Para comprobar no sólo el funcionamiento de la biblioteca, sino también su inserción en otros programas, se desarrolló una aplicación. Todas las pruebas realizadas mostraron un buen desempeño sin errores identificados. Como desarrolló futuro de este trabajo, se prevé la implementación de otras heurísticas de optimización multiobjetivo, tales como los algoritmos genéticos y la optimización de hormiguero. También deberá ser integrada esta biblioteca en aplicaciones para resolver problemas industriales reales, como son la optimización de la sostenibilidad de procesos de fabricación y el diseño de microindustrias alimentarias.

En este artículo se formula un modelo multiobjetivo para seleccionar estrategias de mantenimiento óptimas en sistemas formados por varios elementos interconectados. Las aquí planteadas corresponden al conjunto de acciones eficientes, centradas en maximizar la confiabilidad del sistema y, a su vez, minimizar los costos asociados. La optimización se realiza mediante el uso de algoritmos evolutivos tipo NSGA-II. Para evaluar la confiabilidad del sistema se utiliza un procedimiento basado en simulación de Monte Carlo, que permite analizar sistemas con distintas funciones de desempeño y para configuraciones de componentes diferentes a las clásicas (serie, paralelo, k-out-of-N). La propuesta se analiza para los componentes de un sistema eléctrico de potencia, específicamente las cadenas de aisladores de las líneas de transmisión, y varios escenarios de cálculo. Las estrategias seleccionadas por el modelo priorizan los elementos más importantes, según costo o mantenimiento, y conforman un frente de Pareto aproximado donde el decisor puede seleccionar la más adecuada, de acuerdo con sus intereses.

Copyright of Técnica Industrial: Revista Cuatrimestral de Ingeniería, Industria e Innovación is the property of Fundacion Tecnica Industrial and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Within Taguchi methods, robust parameter design is a widely used tool for quality improvement in processes and products. The loss function is another quality improvement technique with a focus on cost reduction. Traditional Taguchi methods focus on process improvement or optimization with a unique quality characteristic. Analytical approaches for optimizing processes with multiple quality characteristics have been presented in the literature. In this investigation, a case of analysis for two quality characteristics in rubber for shoe sole is presented. A methodology supported in robust parameter design in combined array is used in order to obtain optimal levels in the vulcanization process. Optimization techniques based on the loss function and the use of restricted nonlinear optimization with genetic algorithms are proposed. [ABSTRACT FROM AUTHOR]

Copyright of Revista Avances: Investigación en Ingeniería is the property of Universidad Libre, Facultad de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

En este trabajo, se propone un problema de optimización multiobjetivo de potencia activa y reactiva en las barras del sistema, el cual considera los límites operativos, la capacidad de generación, las reservas de potencia reactiva, capacidad de potencia activa, las restricciones de transmisión, y los límites de voltaje del sistema eléctrico de potencia (SEP). El problema de optimización multiobjetivo propuesto se basa en una combinación de la función objetivo1: margen de potencia reactiva y la función objetivo 2: potencia activa, en la barra sujeta a analisis del margen del SEP. Del problema multiobjetivo se obtiene la Frontera de Pareto, que representa la curva PQ (proyección de la curva PV y QV sobre el plano) de la barra en análisis. El modelo del problema de optimización es aplicado a un sistema de prueba para su validación y análisis de resultados. De esta manera se proporciona una medida confiable para evaluar la estabilidad de voltaje y la seguridad del sistema en redes eléctricas considerando los limites máximos de operación del SEP.

En el trabajo se presentan resultados investigativos obtenidos por los autores en la solución de la tarea de selección óptima del equipamiento en medi os de en ergía renovable para satisfacer las necesidades de un territorio y de cada una de sus partes a par tir de las ofertas disponibles en el mercado y de las disponibilidades de recursos energéticos en el territorio. Se realiza un estudio de los trab ajos de investigación existentes, vinculados a la selección de instalaciones energéticas, a partir de lo s que se establecen los antecedentes, se hace un análisis sistémico de la tar ea de sele cción de instalaciones ener géticas, se determina el modelo mate mático conceptual para la tarea, su complejidad y el esquema de su descomposición para las condiciones próximas a las del territorio del municipio Ganda, Benguela, Angola, tomado como caso de estudio del presente trabajo.

Este trabajo aborda la optimización de portafolios teniendo en cuenta restricciones impuestas por los mercados financieros y condiciones de proyectos con exceso de liquidez, como costos de transacción, presupuesto limitado y horizontes de tiempo cortos. Ante estas condiciones, se ha encontrado que los modelos convencionales pueden generar portafolios ineficientes. Por lo tanto, se formula un modelo matemático y se implementa un algoritmo genético multiobjetivo para hallar portafolios eficientes en la Bolsa de Valores de Colombia, minimizando el riesgo y maximizando la rentabilidad. Adicionalmente, se presentan resultados que permiten comparar los portafolios obtenidos con el modelo propuesto y el modelo de media-varianza, mostrando la importancia de los costos de transacción y el presupuesto en la toma de decisiones de inversión.

La selección de parámetros óptimos de corte es un aspecto esencial en la planificación de cualquier proceso de maquinado, principalmente cuando la sostenibilidad es un objetivo primordial. En este trabajo se presenta una optimización multiobjetivo dirigida a producciones sostenibles, con el fin de seleccionar los parámetros de corte óptimos (velocidad, avance y la profundidad de corte) en operaciones de cilindrado multipasada. Como funciones objetivos del proceso fueron considerados: el aspecto económico y el medioambiental, los cuales son pilares representativos de la sostenibilidad. Requerimientos técnicos, tales como: la potencia de corte, las fuerzas y la rugosidad superficial fueron considerados como restricciones. La optimización se llevó a cabo mediante un enfoque a posteriori; donde se obtuvo un conjunto de soluciones no dominadas (también conocidas como frontera de Pareto) que permitió la selección de la combinación más viable de objetivos para las condiciones específicas del taller. Para la optimización se emplearon dos técnicas de gradiente libre: algoritmo genético sin ordenamiento y enjambre de partículas. Se desarrolló un estudio de caso para evaluar el ajuste y la eficiencia de las técnicas propuestas. Los resultados obtenidos mostraron un mejor rendimiento del algoritmo genético usado, en cuanto a: costo computacional y calidad de la frontera de Pareto obtenida. El método propuesto demostró ser muy conveniente para la optimización sostenible del proceso de torneado, a través de una evaluación simultánea de aspectos económicos y ambientales.

Today, in the competitive business world, organizations and companies need to manipulate their business processes. The real key to success in these organizations lies in the design, the proper management of the business processes and the alignment of IT (Information Technology) with the objectives of the organization. The research carried out has the objective of developing a computer tool that allows optimizing the process of the production of the pre-cooked whole lobster belonging to Batabanó Company in the province of Mayabeque. To achieve this objective, the NSGAII and SPEA2 multiobjective optimization algorithms are used with the JMetal framework. For modeling of the process in the BPMN Notation, the Bonita Studio and Yasper tools are used to convert the process into a Petri network, exporting it in a PNML file, which is used by the tool to carry out its optimization. As a result, a computing application is obtained that allows business analysts of the company to obtain possible process improvements in terms of cost and completeness criteria, thus facilitating its better performance in the company. [ABSTRACT FROM AUTHOR]

In this work, butanol production by fermentation using a cyclic vacuum and multi fed-batch operations was evaluated through a process simulation strategy. The saccharification of the substrate, corn stover, was simulated in an integrated reactor with fermentation and in situ product recovery. Optimizations were carried out to achieve as objectives the maximum economic potential and the minimum electricity requirements of the heat-pump system needed for energy integration. Multi-objective optimizations were performed in MATLAB®. It was found that a cyclic vacuum was not required to achieve the maximum economic potential and that a multi-fed batch operating strategy is enough to obtain the maximum economic potential. Using these strategies, the electricity requirements were decreased by 5% and the economic potential was increased by 9% (~4 MM USD/year) when compared to a fed-batch reactor with in situ recovery using only one feed. [ABSTRACT FROM AUTHOR]

This article presents the advances in the design and implementation of a recommendation system for planning the use of household appliances, focused on improving energy efficiency from the point of view of both energy companies and end-users. The system proposes using historical information and data from sensors to define instances of the planning problem considering user preferences, which in turn are proposed to be solved using a multiobjective evolutionary approach, in order to minimize energy consumption and maximize quality of service offered to users. Promising results are reported on realistic instances of the problem, compared with situations where no intelligent energy planning are used (i.e., ‘Bussiness as Usual’ model) and also with a greedy algorithm developed in the framework of the reference project. The proposed evolutionary approach was able to improve up to 29.0% in energy utilization and up to 65.3% in user preferences over the reference methods. [ABSTRACT FROM AUTHOR]

Introduction: The design of spread footings over a lime-treated soil is studied as an important topic in geotechnical and environmental engineering. With the emergence and use of algorithms, it is possible to solve optimization problems in engineering, leading, for example, to decreased amounts of materials, time, energy, and work. Objective: This research aims to optimize the CO2 emission and cost of building spread footings over a treated soil with hydrated lime using the modified simulated annealing algorithm (MSAA). Method: The parameters for shear strength (cohesion and friction angle) was calculated of a silty soil of the Guabirotuba geological formation of Curitiba (Brazil) stabilized with different lime contents (3, 5, 7 and 9%) at different curing times (30, 90, and 180 days). Then with these parameters, the geometry of the spread footings was optimized with MSAA minimizing the cost and CO2 emissions of their construction. For the design constraint of the structures the ultimate bearing capacity of the soil was used as criteria, the settlements produced by the service load, and the base safety factor Results: The results show that most of the problems converge to the same solution for costs and CO2 emissions without depending on curing time and lime content used, due to the solutions being restricted primarily by the maximum permissible settlements. Conclusions: With the increase in lime content, the cohesion of the mixtures increased for all curing times studied ant the friction angle had no major variations in relation to the amount of lime administered or to the curing time employed. Costs and carbon dioxide emissions for spread footing construction converge to the same results. In this sense, 9% lime can be avoided, and small percentages of lime (i.e. 3-5%) are appropriated to ground improvement and reduce the costs of this procedure. On the other hand, the MSAA can be designated as a robust algorithm due to having achieved almost equal results and, in some cases, better results compared with other algorithms to solve problems reported in the literature. [ABSTRACT FROM AUTHOR]

The work presented the multiobjective optimization of a cyclic cold work and aging treatment of an AISI 1045 steel. As a first step, regression models, for relating the technological variables and the micro-hardness parameters (i.e., the hardness increment and penetration), were fitted from an experimental study. The optimization was carried out by using an a posteriori approach, through the simple multiobjective cross entropy method. From the obtained solutions, the best option was chosen depending on the desired predefined conditions, analyzing several cases as possible practical situations. The work showed the effectiveness of the metaheuristic-based multiobjective optimization for selecting the best working parameters in the cyclic cold work and aging treatment, in order to obtain the most convenient microhardness profile. [ABSTRACT FROM AUTHOR]

To choice audio features has been a very interesting theme for audio classification experts. This process is probably the most important to solve the classification problem. In this sense, techniques of Feature Learning generate attributes more appropriate for classification model. Generally these techniques do not depend on knowledge domain and can apply in various types of data. Yet, less agnostic approaches learn a knowledge restricted to the area studded and audio data requires a specific knowledge. Many techniques aim to improve the performance in generation of new acoustic features, among there is the technique based in evolutionary algorithms to explore analytical space of function. Despite the efforts made, there are still opportunities for improvement. This work proposes and evaluates a multi-objective alternative to the exploitation of analytical audio features. Experiments were arranged to validate the method, with the help a computational prototype implementing the proposed solution. Then it was verified the model effectiveness and was shown there is still opportunity for improvement in the chosen segment. [ABSTRACT FROM AUTHOR]

Enhancing efficiency in Municipal Solid Waste (MSW) management is crucial for local governments, which are generally in charge of collection, since this activity explains a large proportion of their budgetary expenses. The incorporation of decision support tools can contribute to improve the MSW system, specially by reducing the required investment of funds. This article proposes a mathematical formulation, based on integer programming, to determine the location of garbage accumulation points while minimizing the expenses of the system, i.e., the installment cost of bins and the required number of visits the collection vehicle which is related with the routing cost of the collection. The model was tested in some scenarios of an important Argentinian city that stills has a door-to-door system, including instances with unsorted waste, which is the current situation of the city, and also instances with source classified waste. Although the scenarios with classified waste evidenced to be more challenging for the proposed resolution approach, a set of solutions was provided in all scenarios. These solutions can be used as a starting point for migrating from the current door-to-door system to a community bins system. [ABSTRACT FROM AUTHOR]

Nowadays, autonomous systems that incorporate renewable energy sources and energy storage systems play a fundamental role as a solution to energy supply problems. These systems are a good alternative, mainly in remote areas of the electricity network such as islands. This paper analyzes the cost of battery life, operation and maintenance costs and the uncertainty costs associated with renewable energy agents for an autonomous microgrid developed on Dong-fushan Island in China, all in order to obtain the adequate parameters for the optimum operation of the same, without forgetting the characteristics of useful life in specific lead-acid batteries. It is intended, through the non-dominant algorithm of genetic classification (NSGA II), to achieve maximization of the aforementioned variables: useful life of the batteries, and the reduction of the generation cost, proposing a multi-objective optimization. In addition to the above, the costs of uncertainty associated with renewable wind energy are included. [ABSTRACT FROM AUTHOR]

This paper presents a method to calculate multiple voltage stability limits (VSLs), voltage stability margins (VSMs), and voltage stability curves (VSCs). A multiobjective metaheuristic algorithm was used to change real and reactive powers of loads and generators and perform contingencies of the network elements. The maximum values of real and reactive powers were selected using fast non-dominated sorting and crowding distance techniques, combined with a conventional power flow (PF), and referred to in this paper as NSPF. The results show that the method can select and assess multiple maximum real and reactive powers allowed for a bus, an area, or the power system and that it can consider changes in load, generation, and contingencies. Results were validated using the PF and continuation power flow methods. The method finds a large number of VSCs in less time than the conventional methods and estimates multiple VSLs and VSMs. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ingeniería - UPTC is the property of Universidad Pedagogica y Tecnologica de Colombia, Facultad de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The work presents research results obtained by the authors in the solution of optimal selection of renewable energy equipment task to meet the needs of a territory and each one of its parts from the offers available in the market and of the availability of energy resources in the territory. It is carried out a study of the existent research works, linked to the selection of energy installations, starting from those the antecedents settle down. A systemic analysis of the task is made, the conceptual mathematical model for the task, it complexity and the decomposition outline were determined for conditions close to the those that are present in the municipality of Ganda, Benguela province, Angola, taken as Study Case. [ABSTRACT FROM AUTHOR]

Copyright of Universitas Scientiarum is the property of Pontificia Universidad Javeriana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a parameter set for the generalized-α class of time integration methods that allows to keep the beneficial damping properties of the algorithm including controllable dissipation of high frequency oscillations as well as second order of convergence for a large problem class. The benefits of the new parameter set are that the introduction of an additional parameter allows to lessen the overshoot behavior in the transient phase leading to a more robust time integration. To assess the performance of the new parameters, we give a numerically tractable definition of overshoot for the class of algorithms and analyze the methods in terms of a multicriteria optimization problem taking overshoot as well as beneficial damping into account. Using nonlinear scalarization techniques, we can then obtain full insight in how well the methods can perform and evaluate the classical as well as the new parameter sets. Some numerical test examples illustrate the findings. [ABSTRACT FROM AUTHOR]

Selecting optimal cutting parameters is a very important task in any machining process planning, especially when sustainability is in the sight. This paper presents a multi-objective optimization focused on sustainable productions, for selecting optimal cutting parameters (cutting speed, feed, and depth of cut) in multi-pass cylindrical turning operations. Both, the economic and environmental pillars of sustainability are considered as optimization targets. Technical requirements, such as cutting power, forces and surface roughness, are also taken into account as constraints. Optimization was carried out through a posteriori approach, where a set of non-dominated solutions, also known as Pareto front, were obtained and, then, the most suitable combination of targets is selected for the specific workshop conditions. Two gradient-free optimization techniques were used for carrying out the optimization: the non-sorting genetic algorithm II and the multi-objective particle swarm optimization. A study case was carried out not only for evaluating the fitness of the proposed approach but also for comparing the performance of the considered techniques. The outcomes showed a better performance by the genetic algorithms, in both the computational efficiency and the quality of the obtained Pareto front. The proposed approach demonstrated its convenience for sustainability optimization of machining processes, giving a simpler way for analyzing simultaneously the economic and environmental aspects of sustainability. [ABSTRACT FROM AUTHOR]

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The wireless mesh network is the type of network more used currently, the same as show interesting challenges in the topic of investigation. One of the challenges is as improve the capability of a network to provide better service to selected network traffic over various technologies. This requires keep in mind variables such as delay, jitter, packet loss and band width. This type of challenge where is necessary to optimize several variables becomes a combinatorial and computational complex problem. A method for solve this type of problems are the genetic algorithms. In this work we show a multiobjective genetic algorithm for quality of service in routing in wireless mesh networks. The proposed genetic algorithm besides has the advantage of use the Pareto frontier algorithm with the divide and conquer technique and the knee algorithm for to select an only solution of the problem. The paths found for the proposed algorithm demonstrate that achieve the purpose to improve the quality of service in routing in wireless mesh networks.

This article describes two new methodologies for optimal distribution system planning considering automatic reclosers to improve reliability indices. The first methodology proposed in this paper employs a two-stage cascade strategy to solve the problem. In the first stage a mono-objective Tabu Search (TS) algorithm is used to find the optimal distribution system topology. The installation cost of new elements, increasing capacity on existing elements and operational costs associated with technical losses are considered. In the second stage a Non-Dominated Sorting Genetic Algorithm known as NSGA II is employed, considering the optimal distribution system topology found by the first stage, and thus, solving the problem of optimal location of automatic reclosers in distribution systems. The non-supplied energy level of the network and installation cost of reclosers are considered as objective functions; and the min-max criterion to select a point on the optimal Pareto front is used. In the second methodology, a master-slave based optimization algorithm is used to find the best network topology including the associated to automatic reclosers. Master algorithm is a TS algorithm which is responsible to direct the optimization process through solution space, whereby, for each neighbor generated, the slave algorithm NSGA II is applied and min-max criterion is used to determine a configuration of reclosers for actual network topology. The proposed methodologies are applied on three-phase version of IEEE 34 nodes test feeder, with high quality results. [ABSTRACT FROM AUTHOR]

This paper reports the use of an orthogonal array for multi-objective optimization of the submerged arc welding process of JIS 3116 steel, by usingthe genetic algorithm based-method. Three process parameters were selected as independent variables: arc voltage, current intensity and welding speed. In addition, the geometric parameters of the welded joint were considered as constraints of the optimization problem. Two objective functions were selected: the deposition rate and dilution. Finally, the obtained outcomes were arranged in graphical form (Pareto's front) and analyzed to make the proper decision for different process preferences, allowing the comparison with the parameters used before the optimization task. [ABSTRACT FROM AUTHOR]