A typical refinery consists of the production system and the utility system that are routinely optimized in a hierarchical procedure. To meet the demand for higher profit and energy utilization, it is imperative to integrate the two systems for refinery-wide simultaneous optimization, although the integrated model results in a mixed-integer nonlinear programming (MINLP) problem in which the bilinear terms for the correlation of the blending process and gas emission directly result in inconsistency between solution quality and time. The main aim of this work is to propose a solution strategy based on heuristics to decompose the integrated model of the two systems into a mixed-integer linear programming (MILP) model and a nonlinear programming (NLP) model which are then solved iteratively through variables transferring to further reduce the solution time. The solution of the traditional sequential method is incorporated to generate better initial estimates for the decomposed model to gain better solution quality and efficiency. The proposed solution approach is compared with the basic sequential method and the standard MINLP solvers. The results obtained in two scenarios of a case study from a real refinery demonstrate the effectiveness of the proposed decomposition strategy.