WEA 2015: En vía de construír un espacio para mejores Ingenieros

Workshop on Engineering Applications 2015: On the Road to Build a Space for Better EngineersWEA 2015: En vía de construír un espacio para mejores Ingeniero

WEA 2015: En vía de construír un espacio para mejores Ingenieros

Workshop on Engineering Applications 2015: On the Road to Build a Space for Better EngineersWEA 2015: En vía de construír un espacio para mejores Ingeniero

Fuzzy Linear Programming with Interval Type-2 fuzzy constraints

Doctorad

FRand: Toolbox de MATLAB para Simulación de Números Aleatorios Difusos

Context: This paper presents a MATLAB code implementation and the GUI (General User Interface) for fuzzy random variable generation. Based on previous theoretical results and applications, a MATLAB toolbox has been developed and tested for selected membership functions. Method: A two–step methodology was used: i) a MATLAB toolbox was implemented to be used as interface and ii) all .m functions are available to be used as normal code. The main goal is to provide graphical and code–efficient tools to users. Results: The main obtained results are the MATLAB GUI and code. In addition, some experiments were ran to evaluate its capabilities and some randomness statistical tests were successfully performed. Conclusions: Satisfactory results were obtained from the implementation of the MATLAB code/toolbox. All randomness tests were accepted and all performed experiments shown stability of the toolbox even for large samples (>10.000). Also, the code/toolbox are available online. Acknowledgements: The authors would like to thank to the Prof. M Sc. Miguel Melgarejo and Prof. Jos´e Jairo Soriano–Mendez sincerely for their interest and invaluable support, and a special gratefulness is given to all members of LAMIC.Contexto: Este trabajo presenta una implementaci´on de c´odigo de MATLAB y un GUI (interfaz de usuario) para la generaci´on de variable aleatoria difusa. Basados en resultados te´oricos y aplicaci´on previos, un toolbox de MATLAB fu´e desarrollado y validado para diferentes funciones de pertenencia. M´etodo: Una metodolog´ıa de dos pasos ha sido implementada: i) un toolbox de MATLAB es implementado para usarse como interfaz y ii) todas las funciones .m est´an disponibles para usarse como c´odigo normal. La meta principal es proveer herramientas gr´aficas y de c´odigo a los usuarios Resultados: Los resultados principales de este trabajo son el MATLAB GUI y el c´odigo subyacente. Adicionalmente, algunos experimentos fueron realizados para evaluar las capacidades del toolbox, y algunas pruebas estad´ısticas de aleatoriedad fueron realizadas con ´exito. Conclusiones: Resultados satisfactorios de la implementaci´on del c´odigo/toolbox de MATLAB fueron obtenidos. Todos los tests estad´ısticos fueron aceptados y todos los experimentos realizados mostraron que el toolbox es estable a´un para tama˜nos de muestra grande (>10.000). Adicionalmente, el toolbox/c´odigo est´a disponible online. Agradecimientos: Los autores agradecen sinceramente a los Prof. M Sc. Miguel Melgarejo y Prof. Jos´e Jairo Soriano–Mendez por su inter´es e invaluable apoyo, y agradecen de manera especial a todos los miembros del Grupo LAMIC

Una aproximación a través del modelo p-robusto para el problema estocástico de ruteo e inventario

Context: Approaches to logistics solutions through mathematical optimization are widely studied in the literature given their importance for business operations and their computational complexity. In this way, studying the uncertainty associated to operations is a key factor in modeling and decision-making. Method: A stochastic mathematical model is proposed for the Inventory Routing Problem (IRP), considering scenarios with variation in the demands. To obtain a suitable approach, a p-robustness approach and the reformulation of the classical IRP are presented. Results: The performed experiments show the benefits of including uncertainty through a p-robust approach when they are analyzed within an instance of the IRP. Moreover, given the selected modeling, the benefits of combining the approaches can be analyzed. Conclusions: The development of stochastic approaches for decision-making applied to the IRP allow analysts to handle uncertainty and also reduce the complexity of decision when combining different types of problems (Routing + Inventory) in the same model.Contexto: Las aproximaciones de soluciones logísticas a través de la optimización matemática son altamente estudiadas en la literatura debido a su importancia en las operaciones de las compañías y su complejidad computacional. En este sentido, el estudio de la incertidumbre asociada a la operación es un factor fundamental del modelamiento y la toma de decisiones. Método: Un modelo matemático estocástico es propuesto para el problema combinado de ruteo e inventario (IRP), considerando escenarios de variaciones en la demanda. Para obtener un enfoque adecuado, se presenta una aproximación de p-robusto y la reformulación del problema clásico de aplicación. Resultados: Los experimentos realizados muestran los beneficios de incluir la incertidumbre a través de la aproximación de p-robusto cuando se analizan en el marco de una instancia del IRP. También, dado el tipo de modelado seleccionado, se pueden analizar los beneficios de combinar las aproximaciones. Conclusiones: El desarrollo de aproximaciones estocásticas de toma de decisiones aplicadas al problema IRP permite a los analistas gestionar la incertidumbre y reducir la complejidad de las decisiones cuando se combinan diferentes tipos de problemas (Ruteo + Inventario) en un mismo modelo.

Hacia la solución de juegos matriciales con incertidumbre difusa Tipo-2 a través de optimización lineal

This paper presents some theoretical and computing considerations about how to deal with fuzzy uncertainty in the parameters of the classical games model. Indeed, when multiple experts are involved in a game situation, then their opinions lead to have uncertainty since most of the times they are not agree to each others. This kind of uncertainty can be modeled using Type-2 fuzzy sets, which implies a specialized methods and sub-models.Some considerations about the use of Type-2 fuzzy sets and what does this imply when computing solutions, are presented. A general model which includes this kind of uncertainty is defi ned on the base of the extension principle and α-cuts representation theorem. A possible way for solving this model is glimpsed and put down for discussion and implementation.Este artículo presenta algunas consideraciones computacionales y teóricas acerca de cómo incluír incertidumbre difusa en los parámetros de un problema clásico de juegos. De hecho, cuando varios expertos están involucrados en un problema de juegos, todas sus opiniones llevan a pensar en una fuente incertidumbre, ya que muchas veces esos expertos no están de acuerdo entre sí. Ese tipo de incertidumbre puede modelarse mediante conjuntos difusos Tipo-2, lo que implica usar modelos y métodos especiales para llegar a una respuesta adecuada.Se presentan algunos aspectos importantes acerca del cálculo de soluciones en presencia de este tipo de incertidumbre. Un modelo general que incluye incertidumbre difusa Tipo-2 es presentado, el cual se basa en el principio de extensión y el teorema de representación de α-cortes. Un posible método de solución es puesto a consideración para discusión e implementación

El Workshop on Engineering Applications 2016: Avanzando hacia el Reconocimiento Internacional

Workshop on Engineering Applications 2016: A Step Closer Towards International RecognitionLanguage: English El Workshop on Engineering Applications 2016: Avanzando hacia el reconocimiento internacionalIdioma: Inglé

FRand: MATLAB Toolbox for Fuzzy Random Number Simulation

Context: This paper presents a MATLAB code implementation and the GUI (General User Interface) for fuzzy random variable generation. Based on previous theoretical results and applications, a MATLAB toolbox has been developed and tested for selected membership functions. Method: A two–step methodology was used: i) a MATLAB toolbox was implemented to be used as interface and ii) all .m functions are available to be used as normal code. The main goal is to provide graphical and code–efficient tools to users. Results: The main obtained results are the MATLAB GUI and code. In addition, some experiments were ran to evaluate its capabilities and some randomness statistical tests were successfully performed. Conclusions: Satisfactory results were obtained from the implementation of the MATLAB code/toolbox. All randomness tests were accepted and all performed experiments shown stability of the toolbox even for large samples (>10.000). Also, the code/toolbox are available online. Acknowledgements: The authors would like to thank to the Prof. M Sc. Miguel Melgarejo and Prof. Jos´e Jairo Soriano–Mendez sincerely for their interest and invaluable support, and a special gratefulness is given to all members of LAMIC

CompareML: A Novel Approach to Supporting Preliminary Data Analysis Decision Making

There are a large number of machine learning algorithms as well as a wide range of libraries and services that allow one to create predictive models. With machine learning and artificial intelligence playing a major role in dealing with engineering problems, practising engineers often come to the machine learning field so overwhelmed with the multitude of possibilities that they find themselves needing to address difficulties before actually starting on carrying out any work. Datasets have intrinsic properties that make it hard to select the algorithm that is best suited to some specific objective, and the ever-increasing number of providers together make this selection even harder. These were the reasons underlying the design of CompareML, an approach to supporting the evaluation and comparison of machine learning libraries and services without deep machine learning knowledge. CompareML makes it easy to compare the performance of different models by using well-known classification and regression algorithms already made available by some of the most widely used providers. It facilitates the practical application of methods and techniques of artificial intelligence that let a practising engineer decide whether they might be used to resolve hitherto intractable problems. Thus, researchers and engineering practitioners can uncover the potential of their datasets for the inference of new knowledge by selecting the most appropriate machine learning algorithm and determining the provider best suited to their data