Discrete-Event Simulation (DES) is a technique in which the simulation engine plays a history following a chronology of events. The technique is called ”discrete-event” because the processing of each event of the chronology takes place at discrete points of a continuous time-line. In computer implementations, an event could be represented by a message, and a time occurrence. The message data type is usually defined as part of the model and the simulator algorithms do not operate with them. Opposite is the case of time variables; simulator has to interact actively with them for reproducing the chronology of events over R+, which is usually represented by approximated data types as floating-point. The approximation of time values in the simulation can affect the time-line preventing the generation of correct results. In addition, it is common to collect data from real systems to predict future phenomena, for example for weather forecasting. When collecting data using metrological instruments and procedures, the measurement results include uncertainty quantifications, usually defined as intervals. However, sometimes, answering a question requires evaluating the results of all values in the uncertainty interval. This thesis proposes data types for handling representation of time properly in DES, including irrational and periodic time values. Moreover, we propose a method for obtaining every possible simulation result of DES models when feeding them events with uncertainty quantification on their time component.