Date of Award


Embargo Period


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Chemical Engineering


Ignacio E. Grossmann

Second Advisor

Lorenz Biegler

Third Advisor

Nikolaos Sahinidis

Fourth Advisor

John Hooker

Fifth Advisor

Willem-Jan van Hoeve


This thesis deals with the development of mathematical models and algorithms for optimizing re finery crude-oil operations schedules. The problem can be posed as a mixed-integer nonlinear program (MINLP), thus combining two major challenges of operations research: combinatorial search and global optimization.

First, we propose a uni ed modeling approach for scheduling problems that aims at bridging the gaps between four different time representations using the general concept of priority-slots. For each time representation, an MILP formulation is derived and strengthened using the maximal cliques and bicliques of the non-overlapping graph. Additionally, we present three solution methods to obtain global optimal or near-optimal solutions. The scheduling approach is applied to single-stage and multi-stage batch scheduling problems as well as a crude-oil operations scheduling problem maximizing the gross margin of the distilled crude-oils.

In order to solve the crude-oil scheduling MINLP, we introduce a two-step MILP-NLP procedure. The solution approach benefi ts from a very tight upper bound provided by the first stage MILP while the second stage NLP is used to obtain a feasible solution.

Next, we detail the application of the single-operation sequencing time representation to the crude-oil operations scheduling problem. As this time representation displays many symmetric solutions, we introduce a symmetry-breaking sequencing rule expressed as a deterministic finite automaton in order to efficiently restrict the set of feasible solutions.

Furthermore, we propose to integrate constraint programming (CP) techniques to the branch & cut search to dynamically improve the linear relaxation of a crude-oil operations scheduling problem minimizing the total logistics costs expressed as a bilinear objective. CP is used to derived tight McCormick convex envelopes for each node subproblem thus reducing the optimality gap for the MINLP.

Finally, the refinery planning and crude-oil scheduling problems are simultaneously solved using a Lagrangian decomposition procedure based on dualizing the constraint linking crude distillation feedstocks in each subproblem. A new hybrid dual problem is proposed to update the Lagrange multipliers, while a simple heuristic strategy is presented in order to obtain feasible solutions to the full-space MINLP. The approach is successfully applied to a small case study and a larger refinery problem.