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This paper describes a methodology which combines elements of statistics, probability, mathematical programming, simulation, multiobjective optimization and metaheuristics, to analyze management problems in a health care context. We apply this approach to a staffing problem in a primary care center, taking into account both cost and service quality(More)
In a previous paper, we developed an accurate simulation model of an Intensive Care Unit to study bed occupancy level (BOL). By means of accurate statistical analysis we were able to fit models to arrivals and length-of-stay of patients. We model doctors' patient discharge decisions and define a set of rules to determine the conditions for earlier or(More)
In recent years, growing attention has been paid to the use of renewable resources to produce electricity. One of the main drawbacks of generating electricity through, say, wind power, however, is random input, which obviously results in random output. This means that peak output does not always coincide with peak demand. Nevertheless, demand drives prices,(More)
In this paper we intend to illustrate how Functional Data Analysis (FDA) can be very useful for simulation input modelling. In particular, we are interested in the estimation of the cumulative mean function of a <i>non-homogeneous</i> Poisson Process (NHPP). Both parametric and nonparametric methods have been developed to estimate it from observed(More)
Energy systems based on some natural renewal sources have the drawback of a random input, making them a non reliable supplier of energy. The regulation of the produced energy requires the introduction of new equipment able to storage this energy. The advantage of these transformation-storage systems is that the energy can be sold when the demand is higher(More)
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