Uncertainty is one of the intrinsic characteristics of natural phenomena, and optimization is not an exception here. Parameters in an optimization problem may not be known exactly or may experience variation after finding an optimal solution. In this paper we consider the linear optimization problem in an uncertain environment. It is assumed that the problem parameters are provided by an expert in the domain. Uncertainty Theory initiated by Liu in 2007, an axiomatic framework for modeling of human reasoning, is the underlying idea of the approach here. Two different uncertain linear programs are presented in this study; moreover, feasibility and optimality of the models as well as relations between these two models are investigated. Obtained results are depicted in two illustrative simple examples.