In this paper we present a robust real-time optimization method for the online linear oilblending process. The blending process consists in determining the optimal mix ofcomponents so that the final product satisfies a set of specifications. We examinedifferent sources of uncertainty inherent to the blending process and show how to addressthis uncertainty applying the Robust Optimization techniques. The polytopal structure ofour problem permits a simplified robust approach. Our method is intended to avoidreblending and we measure its performance in terms of the blend quality giveaway andfeedstocks prices. The difference between the nominal and the robust optimal values (theprice of robustness) provides a benchmark for the cost of reblending which is difficult toestimate in practice. This new information can be used to adjust the level of conservatismin the model. We analyze the feasibility of a blend to be produced from a set offeedstocks when the heel of a previous blend is used in the composition of the new blend.Additional critical information for the control system is then produced.


We deal here with a scheduling problem GPPCSP (Generalized Parallelism and Preemption Constrained Scheduling Problem) which is an extension of both the well-known Resource Constrained Scheduling Problem and the Scheduling Problem with Disjunctive Constraints. We first propose a reformulation of GPPCSP: according to it, solving GPPCSP means finding a vertex of the Feasible Vertex Subset of an Antichain Polyhedron. Next, we state several theoretical results related to this reformulation process and to structural properties of this specific Feasible Vertex Subset (connectivity, ...). We end by focusing on the preemptive case of GPPCSP and by identifying specific instances of GPPCSP which are such that any vertex of the related Antichain Polyhedron may be projected on its related Feasible Vertex Subset without any deterioration of the makespan. For such an instance, the GPPCSP problem may be solved in a simple way through linear programming.

A programming tactic involving polyhedra is reported that has been widely applied in the polyhedral analysis of (constraint) logic programs. The method enables the computations of convex hulls that are required for polyhedral analysis to be coded with linear constraint solving machinery that is available in many Prolog systems.

The classical Schläfli formula relates the variations of the dihedral angles of a smooth family of polyhedra in a space-form to the variation of the enclosed volume. We give higher analogues of this formula: for each p, we prove a simple formula relating the variation of the volumes of the codimension p faces to the variation of the ‘curvature’ – the volumes of the duals of the links in the convex case – of codimension p+2 faces. It is valid also for ideal polyhedra, or for polyhedra with some ideal vertices. This extends results of Suárez-Peiró. The proof is through analoguous smooth formulas. Some applications are described.