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Abstract and Applied Analysis
Volume 2014, Article ID 821707, 16 pages
Research Article

Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities

1Department of Mechanical, Energy and Materials Engineering, Public University of Navarre, Campus of Tudela, 31500 Tudela, Spain
2Department of Electrical Engineering, University of La Rioja, 26006 Logroño, Spain
3Department of Mechanical Engineering, University of La Rioja, 26006 Logroño, Spain

Received 21 February 2014; Accepted 30 April 2014; Published 11 June 2014

Academic Editor: Guanglu Zhou

Copyright © 2014 Juan-Ignacio Latorre-Biel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 paradigm of the PN for describing DEDS, 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.