Análisis e implementación de algunas técnicas de simulación
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2016-07
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Jaén: Universidad de Jaén
Resumen
[ES] El uso de las técnicas de simulación está muy extendido y resulta clave para la
toma de decisiones en el mundo empresarial, siendo especialmente aplicadas
en ámbitos en los que existen altas dosis de riesgo e incertidumbre. Esta
herramienta se basa en el desarrollo de un modelo lógico-matemático con el
que se busca imitar un sistema, con el objetivo de comprender su
comp01tamiento y evaluar nuevas estrategias, siendo utilizada también como
una técnica de muestreo controlado.
En este trabajo se analizan diversos aspectos clave para el uso con-ecto de las
técnicas de simulación. En particular, se estudian distintos métodos para la
generación de muestras de variables aleatorias usuales, así como su
implementación en R. Para ilustrar la aplicación de dichos métodos, se incluye
un ejemplo práctico en el que se estudian los costes de tratamiento de un grupo
de clientes de una compañía de seguros médicos. Las técnicas de simulación
permiten obtener una buena aproximación de la distribución de costes para
este tipo de clientes, de forma que resulte de utilidad a la compañía de seguros
médicos para fijar su política de precios.
[EN] The simulation techniques are widely used and they are essential for strategic decision making in business, especially in those fields involving high risk and uncertainty. This tool 1s based on the development of a logical and mathematical model which emulates a system in order to understand its performance and evaluate new strategies, and it is also used as a controlled sampling technique. In this paper, sorne key aspects for the correct use of simulation techniques are analyzed. Particularly, we study different methods to generate samples ofusual random variables, as well as their implementation in R. In order to illustrate the application of these methods, we include a practica} example, where the treatment costs of a group of customers in a medical insurance company are analyzed. Simulation techniques allow us to get a good approximation of the distribution of costs for this kind of customers, thus providing the medical insurance company with useful information to set its pricing policy.
[EN] The simulation techniques are widely used and they are essential for strategic decision making in business, especially in those fields involving high risk and uncertainty. This tool 1s based on the development of a logical and mathematical model which emulates a system in order to understand its performance and evaluate new strategies, and it is also used as a controlled sampling technique. In this paper, sorne key aspects for the correct use of simulation techniques are analyzed. Particularly, we study different methods to generate samples ofusual random variables, as well as their implementation in R. In order to illustrate the application of these methods, we include a practica} example, where the treatment costs of a group of customers in a medical insurance company are analyzed. Simulation techniques allow us to get a good approximation of the distribution of costs for this kind of customers, thus providing the medical insurance company with useful information to set its pricing policy.