This paper proposes a decision-supporting framework for a feeding problem in the petrochemical industry. The problem is concerned with delivering materials from suppliers to plants, unloading and storing in storage tanks, and mixing the materials before directly feeding into main processes. Most of the previous works in the literature have addressed these concepts, based on the assumption that the delivery of raw materials is given and fixed. From a joint investigation with industry partners, we have determined that the purchase of feedstock and its delivery also are critical issues in the feed scheduling problem of real-world plants. Thereafter, this paper takes into account previously addressed issues separately and simultaneously, to increase the overall efficiency. The corresponding decision-making problem is mathematically transformed to a mixed-integer nonlinear programming (MINLP) problem. The solution of the problem is computed using the iterative framework between that of a relaxed mixed-integer linear programming (MILP) problem and that of a nonlinear programming (NLP) problem, to prevent compositional discrepancy. An industry-coworked example of the naphtha case is presented, to illustrate the applicability of the proposed framework.