Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities
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 mak...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/821707 |
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| author | Juan-Ignacio Latorre-Biel Emilio Jiménez-Macías Mercedes Pérez de la Parte Julio Blanco-Fernández Eduardo Martínez-Cámara |
| author_facet | Juan-Ignacio Latorre-Biel Emilio Jiménez-Macías Mercedes Pérez de la Parte Julio Blanco-Fernández Eduardo Martínez-Cámara |
| author_sort | Juan-Ignacio Latorre-Biel |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b7770e9a68bf4c0e8b66f7d98ab0c5d8 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-b7770e9a68bf4c0e8b66f7d98ab0c5d82025-02-03T05:46:23ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/821707821707Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing FacilitiesJuan-Ignacio Latorre-Biel0Emilio Jiménez-Macías1Mercedes Pérez de la Parte2Julio Blanco-Fernández3Eduardo Martínez-Cámara4Department of Mechanical, Energy and Materials Engineering, Public University of Navarre, Campus of Tudela, 31500 Tudela, SpainDepartment of Electrical Engineering, University of La Rioja, 26006 Logroño, SpainDepartment of Mechanical Engineering, University of La Rioja, 26006 Logroño, SpainDepartment of Mechanical Engineering, University of La Rioja, 26006 Logroño, SpainDepartment of Mechanical Engineering, University of La Rioja, 26006 Logroño, SpainArtificial 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.http://dx.doi.org/10.1155/2014/821707 |
| spellingShingle | Juan-Ignacio Latorre-Biel Emilio Jiménez-Macías Mercedes Pérez de la Parte Julio Blanco-Fernández Eduardo Martínez-Cámara Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities Abstract and Applied Analysis |
| title | Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities |
| title_full | Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities |
| title_fullStr | Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities |
| title_full_unstemmed | Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities |
| title_short | Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities |
| title_sort | control of discrete event systems by means of discrete optimization and disjunctive colored pns application to manufacturing facilities |
| url | http://dx.doi.org/10.1155/2014/821707 |
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