Business Process Management is the science of observing how the work is performed in a given organization ensuring consistent products and seeking opportunities for improvement. Currently, most of the processes are performed in frameworks, many with log files, in which a large amount of data is available. These data can be explored in different ways and with different objectives, giving rise to the Process Mining area. Although many of these data informs how resources are allocated for each activity, the major focus of previous work is on the discovery process techniques and process compliance. In this thesis a petri net model that incorporates resource allocation is proposed exploring the properties of this type of modeling, such as the definition of all possible states. As a model validation, it is applied in a comparative study between two resource allocation policies, one considering the expertise of each resource and other with a more generalist allocation. The arrival of new cases and the resource-activity pair execution time were estimated by Monte Carlo simulations with exponential probability distribution. Thus, for the evaluation of each policy a scoring system was used considering the progress of the process and the total execution time.