Research Article Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities Juan-Ignacio Latorre-Biel, 1 Emilio Jiménez-Macías, 2 Mercedes Pérez de la Parte, 3 Julio Blanco-Fernández, 3 and Eduardo Martínez-Cámara 3 1 Department of Mechanical, Energy and Materials Engineering, Public University of Navarre, Campus of Tudela, 31500 Tudela, Spain 2 Department of Electrical Engineering, University of La Rioja, 26006 Logro˜ no, Spain 3 Department of Mechanical Engineering, University of La Rioja, 26006 Logro˜ no, Spain Correspondence should be addressed to Emilio Jim´ enez-Mac´ ıas; emilio.jimenez@unirioja.es Received 21 February 2014; Accepted 30 April 2014; Published 11 June 2014 Academic Editor: Guanglu Zhou Copyright © 2014 Juan-Ignacio Latorre-Biel et al. his 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. Artiicial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively applied in the industrial production sector. he 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 unafordable computational resources, constrained by the limited available time required to obtain a solution. With the purpose of improving the eiciency 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. his 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. 1. Introduction Many products and services are produced and ofered to the customers in a global market. his globalization may imply the participation, in the lifecycle of a manufacturing facility, of many and very diverse agents, resources, and information from very diferent and distant regions of the world. As a consequence, the management and operation of a manufacturing system, seen as discrete event dynamic systems, require the consideration of a large number of elements, which participate in a greater or less extent in the production yield. Another consequence of this fact consists of the existence of agents and subsystems, which conigure the manufacturing facility and present independent and parallel stages in their evolution, as well as multiple mutual relationships, such as sharing common resources or competing for them. Hence, the behavior of the complete production system may be very complex, despite the fact that the structure and evolution of the subsystems, analyzed independently, might be simple [1, 2]. Some of the components that may conigure a subsys- tem in a manufacturing process are lexible manufacturing systems (FMS), machining centers, conveyor belts or roller tables, automatic guided vehicles (AGV), robots, bufers, or combinations of the previous ones [3, 4]. On the other hand, a manufacturing facility presents a number of controllable parameters, which can also be called freedom degrees [5]. hese freedom degrees allow the decision maker to manage the manufacturing facility or Hindawi Publishing Corporation Abstract and Applied Analysis Volume 2014, Article ID 821707, 16 pages http://dx.doi.org/10.1155/2014/821707