Solving dynamic optimization infeasibility problems

Dynamic real-time optimization (DRTO) systems sometimes fail when solving intrinsic optimization problems. There are situations where the solution is infeasible due to the initial conditions, constraint changes during operation, or even the presence of conflicts on constraint specifications. By using a goal programming approach, this work proposes a method to solve these infeasibilities by reformulating the differential-algebraic optimization problem as a multi-objective dynamic optimization problem with eywords: ynamic optimization nfeasibility ulti-objective optimization path constraint relaxations. Three examples were solved exploring the characteristics of such infeasibility problems. The results demonstrate the ability of the proposed method in identifying and relaxing the constraint violations, increasing the robustness of DRTO systems. © 2011 Elsevier Ltd. All rights reserved. eal-time optimization onstraint relaxation