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<p>Current gasoline blend planning practice is to optimize blend plans via discrete-time multi-period NLP or MINLP models and schedule blends via interactive simulation. Solutions of multi-period models using discrete-time representation typically have different blend recipes for each time period. In this work, the concept of an inventory pinch point is introduced and used it to construct a new decomposition of the multi-period MINLP problems: at the top level nonlinear blending problems for periods delimited by the inventory pinch points are solved to optimize multi-grade blend recipes; at the lower level a fine grid multi-period MILP model that uses optimal recipes from the top level is solved in order to determine how much to blend of each product in each fine grid period, subject to minimum threshold blend size. If MILP is infeasible, corresponding period between the pinch points is subdivided and recipes are re-optimized.</p> <p>Two algorithms at the top level are examined: a) multi-period nonlinear model (MPIP) and b) single-period non-linear model (SPIP). Case studies show that the MPIP algorithm produces solutions that have the same optimal value of the objective function as corresponding MINLP model, while the SPIP algorithm computes solutions that are most often within 0.01% of the solutions by MINLP. Both algorithms require substantially less computational effort than the corresponding MINLP model. Reduced number of blend recipes makes it easier for blend scheduler to create a schedule by interactive simulation.</p> / Master of Applied Science (MASc)