This paper presents a computational comparison between the inside-out approach to refinery-wide optimization, which optimizes the model equations with fixed physical properties and updates the approximate physical property models after each time the model equations are optimized, and the full-scale optimization approach, which solves the optimization problem using the simultaneous solution of the model equations and physical property models. For the refinery model in this study, the inside-out approach decreases the computational time (when compared to full-scale optimization) by nearly 1order of magnitude while converging to the same solution obtained using the full-scale optimization approach with a high degree of reliability. By varying the number of units in the optimization case study, the dependence of CPU time with the number of model equations was assessed for the full-scale and inside-out methods. Extrapolation of these results to a large integrated refinery (i.e., 1 million model equations) indicates that the full-scale approach would require approximately 80 times as much CPU to reach a converged solution as the inside-out optimization approach.