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Book ChapterDOI

Monte Carlo Simulation

01 Jan 2014-Vol. 3, pp 241-275
TL;DR: The Monte Carlo Method is used to simulate any process whose growth is affected by random factors and to solve mathematical problems not affected byrandom factors but can be connected to artificially constructed probabilistic model giving rise to solution, otherwise unavailable.
Abstract: The name “Monte Carlo” emerged from the name of the city “Monte Carlo”, famous for its Casino. In the casino there was a roulette where a small button was fixed at the centre of a wheel and the numbers 0–9 were marked at the end of the ten spokes of the wheel. When the button was pressed, the wheel starts rotating. When the wheel stops, a number within 0–9 was marked by a marker. Depending upon the number near the marker, one had to play the game. Thus this mechanical device was the device, first constructed to generate random numbers. The method was theoretically came through the work “The Monte Carlo Method” published by American mathematicians John Von Neumann and Stanslav Ulam in 1949. In spite of the theoretical background this method could not be used in significant scale until the invention of electronic computers. There are two aspects of this method. First it is used to simulate any process whose growth is affected by random factors and second to solve mathematical problems not affected by random factors but can be connected to artificially constructed probabilistic model giving rise to solution, otherwise unavailable. For example, suppose we are to calculate the area A within a unit square in Fig. 10.1.
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Journal ArticleDOI
TL;DR: The results indicate a higher individual tree detection rate and subsequently a more precise estimation of dendrometric parameters for Norway spruce compared to beech trees located in spruce-beech even-aged mixed stands.

32 citations

Journal ArticleDOI
25 Jun 2019
TL;DR: In this article, a model for minimizing maintenance costs of medical equipment using fuzzy logic is presented, based on a plan of Gestion de Mantenimiento of Equipos Medicos.
Abstract: (A model for minimizing maintenance costs of medical equipment using fuzzy logic) Resumen. En este trabajo se presenta un algoritmo basado en logica difusa que modela un Plan de Gestion de Mantenimiento de Equipos Medicos, este se desarrolla en tres etapas: En la primera se genera un inventario funcional, siguiendo los protocolos recomendados por la OMS e informacion de cada equipo. En la segunda, se acoplan tres protocolos de atencion prioritaria, utilizados para seleccionar las funciones de pertenencia del sistema difuso. En la tercera, se genera una familia de escenarios mediante simulacion de Monte Carlo, calculandose el grado de prioridad difuso de mantenimiento para los equipos. Los resultados logran que la seleccion de equipos del plan de mantenimiento anual se realice garantizando la disponibilidad de los equipos prioritarios. El elemento distintivo en este trabajo es la introduccion de la estructura difusa en los algoritmos de Fennigkoh-Smith y el de Wang-Levenson, permitiendo de este modo que la seleccion del equipo medico se realice en forma automatica limitando el error humano en el proceso. Un area de oportunidad consiste en incorporar un proceso de optimizacion de costos de mantenimiento del equipo con una restriccion presupuestaria. Se concluye que el sistema mostrado es amigable y robusto para los fines planteados.

2 citations

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