(Hindawi, 2014) Latorre Biel, Juan Ignacio; Jiménez Macías, Emilio; Pérez de la Parte, Mercedes; Blanco Fernández, Julio; Martínez Cámara, Eduardo; Ingeniería Mecánica, Energética y de Materiales; Mekanika, Energetika eta Materialen Ingeniaritza
Artificial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively applied
in the industrial production sector. The resulting tools produce excellent results in certain cases; however, the NP-hard nature of
many discrete control or decision making problems in the manufacturing area may require unaffordable computational resources,
constrained by the limited available time required to obtain a solution. With the purpose of improving the efficiency of a control
methodology for discrete systems, based on a simulation-based optimization and the Petri net (PN) model of the real discrete
event dynamic system (DEDS), this paper presents a strategy, where a transformation applied to the model allows removing the
redundant information to obtain a smaller model containing the same useful information. As a result, faster discrete optimizations
can be implemented.This methodology is based on the use of a formalism belonging to the paradigmof thePNfor describingDEDS,
the disjunctive colored PN. Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in
the solution space. As an illustration of the methodology proposal, its performance is compared with the classic approach on a case
study, obtaining faster the optimal solution.
