Global optimization for scheduling refinery crude oil operations

In this work we present an Outer-Approximation algorithm to obtain the global optimum of a nonconvex Mixed Integer Nonlinear Programming (MINLP) model for the scheduling of crude oil movement at the front-end of a petroleum refinery. The model relies on a continuous time representation making use of transfer events. The proposed technique focuses on effectively solving a Mixed Integer Linear Programming (MILP) relaxation of the nonconvex MINLP to obtain a rigorous lower bound on the global optimum. Cutting planes derived by spatially decomposing the network are added to the MILP relaxation of the original nonconvex MINLP in order to tighten the lower bound and reduce the solution times for the MILP relaxation. The solution of this problem is used as a heuristic to obtain a feasible solution to the MINLP which serves as an upper bound. The lower and upper bounds are made to converge to within a specified tolerance in the proposed Outer Approximation algorithm. On applying the proposed technique on test examples, significant savings were realized in the computational effort required to obtain the globally optimal solutions and to verify their global optimality. Corresponding author. Tel.: +1-412-268-2230; Fax: +1-412-268-7139. Email address: [email protected] (I.E. Grossmann)