This work proposes the use of pre-defined generalized stochastic Petri net (GSPN) components to model and evaluate outbound logistics. The modeling process takes into account the automatic generation of these stochastic models from high-level end-user-oriented descriptions. The model resultant from this translation is guaranteed to have some expected Petri nets' properties. The Stochastic Logistics Optimizer Tool (SLOT) was implemented with the aim of supporting the modeling process based on a component library. Case studies were carried out and one of them is presented.