Assuming all the parameters as known data with certainty is a highly optimistic assumption in developing optimization model to solve the operations management problems. Facing with noisy, inaccurate, or unspecific data is an inevitable part of dealing with real-world optimization problems for the decision makers in their attempts to reduce variability and showing the overemphasis of the feasibility of optimization models. Robust Optimization technique is a promising optimization approach tackling the impact of uncertainty of input parameters for solving real-world optimization problems. However, its application is limited owing to the complexity of developing the robust models. To address this limitation, we propose a novel transformation formulation approach to be utilized as a standard framework in constructing linear robust programming formulation from their deterministic counterpart. The proposed framework encodes the construction of the mean, expected variability, and expected infeasibility of the objective function in the robust optimization formulation while it is compared with a stochastic programming formulation. The applicability of the presented framework is illustrated through an optimization problem from a healthcare operating room allocation decision model. The results demonstrate the effectiveness of utilizing the developed framework as an easy to understand and simple to implement approach by the decision makers while the optimal solutions are obtained.