The present research work consists in the development of a method where a mathematical model of a fish canning plant installation project is proposed, considering the investment, the production costs, the operating expenses, the financial statements, and as indicators for decision making, the net financial present value and internal rate of financial return. Then, the mathematical model is optimized using a non-linear programming to choose the most convenient investment alternative, then a Monte Carlo simulation is carried out in which random variables of plant and market are considered that allow the investor to estimate the minimum values and máximum that can happen in the financial indicators and finally the discussion of results.

The present research work consists in the development of a method where a mathematical model of a fish canning plant installation project is proposed, considering the investment, the production costs, the operating expenses, the financial statements, and as indicators for decision making, the net financial present value and internal rate of financial return. Then, the mathematical model is optimized using a non-linear programming to choose the most convenient investment alternative, then a Monte Carlo simulation is carried out in which random variables of plant and market are considered that allow the investor to estimate the minimum values and máximum that can happen in the financial indicators and finally the discussion of results.

The use of concrete as a structural element increases year by year. However, this product needs very stringent control of its mechanical properties in order to be uses as structural element. This type of control requires to have very large testing equipment with a load capacity of up to 3.000KN. Production control would benefit greatly from the use of a highly reliable alternative method that would enable the mechanical properties to be found through more easily obtained physical and mechanical properties. The high capacity of artificial neural networks (ANN) to model a broad range of industrial processes makes them a very useful instrument in the concrete industry. In this study, one neural network was developed to obtain the properties of compressive strength. This property was then modeled though the composition of concrete and manufacturing parameters. The network designed, a multila...

En las edificaciones de las viviendas modernas el concreto es un elemento fundamental que interviene. De otro lado en las construcciones de puentes, diques, túneles, esto es en la construcción de estructuras no estándares de la ingeniería civil, el concreto que se utiliza es el de alto rendimiento (CAR) que aparte de los componentes básicos como el agua, cemento, agregados finos y gruesos, contienen otros aditivos cementantes, como las microsílices. El problema es conseguir un recurso tecnológico que ayude a pronosticar la resistencia de CAR a partir de sus datos de fabricación, pero esto es imposible. Sin embargo, se tiene las redes neuronales artificiales que cumplen este papel, que luego de entrenadas se transforman en verdaderas funciones matemáticas que aproximan los valores esperados de las resistencias de las probetas de concreto. El nivel de aproximación se estima por l...

El uso del concreto como elemento estructural va aumentando año tras año. Sin embargo, este producto requiere de unos estrictos controles de calidad sobre sus propiedades mecánicas para el uso como elemento estructural. Este tipo de control implica la existencia de equipos de ensayo con una capacidad de carga de hasta 3.000KN. Sería de gran utilidad para el control de producción la utilización de un método alternativo de gran fiabilidad, que permitiera conocer las propiedades mecánicas a partir de otras propiedades físicas y mecánicas más fáciles de obtener. La alta capacidad de las redes neuronales artificiales (ANN) para modelar los más diversos procesos industriales, las convierte en una herramienta de gran utilidad en el ámbito de la industria del concreto. En este estudio se ha desarrollado una red neuronal para obtener la resistencia a compresión del concreto y se ha...

The use of concrete as a structural element increases year by year. However, this product needs very stringent control of its mechanical properties in order to be uses as structural element. This type of control requires to have very large testing equipment with a load capacity of up to 3.000KN. Production control would benefit greatly from the use of a highly reliable alternative method that would enable the mechanical properties to be found through more easily obtained physical and mechanical properties. The high capacity of artificial neural networks (ANN) to model a broad range of industrial processes makes them a very useful instrument in the concrete industry. In this study, one neural network was developed to obtain the properties of compressive strength. This property was then modeled though the composition of concrete and manufacturing parameters. The network designed, a multila...