Modeling strategy by adaptive genetic algorithm for production reactive scheduling with simultaneous use of machines and AGVs
The problem of production scheduling of manufacturing systems is characterized by the large number of possible solutions. Several researches have been using the Genetic Algorithms (GA) as a search method to solve this problem since these algorithms have the capacity of globally exploring the search...
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| creator | Morandin, O. Kato, E. R. R. Sanches, D. S. Muniz, B. D. |
| description | The problem of production scheduling of manufacturing systems is characterized by the large number of possible solutions. Several researches have been using the Genetic Algorithms (GA) as a search method to solve this problem since these algorithms have the capacity of globally exploring the search space and find good solutions quickly. Since the performance of the GA is directly related to the choice of the parameters of genetic operators, and a bad choice can depreciate the performance, this paper proposes the use of Adaptive Genetic Algorithm to solve this kind of scheduling problem considering the machines and the Automated Guided Vehicles (AGVs). The aim of this paper is to get a good production reactive schedule in order to achieve a good makespan value in a low response obtaining time. The results of this paper were validated in large scenarios and compared with the results of two other approaches. These results are presented and discussed in this paper. |
| doi_str_mv | 10.1109/ICSMC.2009.5346695 |
| format | Conference Proceeding |
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Since the performance of the GA is directly related to the choice of the parameters of genetic operators, and a bad choice can depreciate the performance, this paper proposes the use of Adaptive Genetic Algorithm to solve this kind of scheduling problem considering the machines and the Automated Guided Vehicles (AGVs). The aim of this paper is to get a good production reactive schedule in order to achieve a good makespan value in a low response obtaining time. The results of this paper were validated in large scenarios and compared with the results of two other approaches. These results are presented and discussed in this paper.</description><subject>adaptive genetic algorithm</subject><subject>automated guided vehicle</subject><subject>Flexible manufacturing systems</subject><subject>Genetic algorithms</subject><subject>Job shop scheduling</subject><subject>Manufacturing systems</subject><subject>Optimal scheduling</subject><subject>Processor scheduling</subject><subject>Production systems</subject><subject>scheduling</subject><subject>Space exploration</subject><subject>Transportation</subject><subject>transportation systems</subject><subject>Vehicles</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>9781424427932</isbn><isbn>1424427932</isbn><isbn>9781424427949</isbn><isbn>1424427940</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkDlOAzEARc0SiSTkAtD4AhO8L2U0ghCJiIJFdJFnbE-MZonGHlAK7k4U0lC94j-94gNwg9EcY6TvVvnLOp8ThPScUyaE5mdgpqXCjDBGpGb6HIwJlzLDgvOLfxsll2CMkSCZJuRjBCaHjNJICIWuwCTGT4QIYliNwc-6s64ObQVj6k1y1R4We2is2aXw5WDlWpdCCU1ddX1I2wb6roe7vrNDmULXwt6Z8mjGcuvscCx9H0QYQzPUybSuGyIcooOdh40pt6F1EZrWwsXyPV6DkTd1dLMTp-Dt4f41f8yenperfPGUBSx5yiT3ypPCSc6URcJg5QtGS4-V5Uj5siQFt07oAy2nhkpKGOLcC-ol58jTKbj96wbn3GbXh8b0-83pVvoLGRRoMw</recordid><startdate>200910</startdate><enddate>200910</enddate><creator>Morandin, O.</creator><creator>Kato, E. 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D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Morandin, O.</au><au>Kato, E. R. R.</au><au>Sanches, D. S.</au><au>Muniz, B. D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Modeling strategy by adaptive genetic algorithm for production reactive scheduling with simultaneous use of machines and AGVs</atitle><btitle>2009 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2009-10</date><risdate>2009</risdate><spage>704</spage><epage>709</epage><pages>704-709</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>9781424427932</isbn><isbn>1424427932</isbn><eisbn>9781424427949</eisbn><eisbn>1424427940</eisbn><abstract>The problem of production scheduling of manufacturing systems is characterized by the large number of possible solutions. Several researches have been using the Genetic Algorithms (GA) as a search method to solve this problem since these algorithms have the capacity of globally exploring the search space and find good solutions quickly. 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| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1062-922X |
| ispartof | 2009 IEEE International Conference on Systems, Man and Cybernetics, 2009, p.704-709 |
| issn | 1062-922X 2577-1655 |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | adaptive genetic algorithm automated guided vehicle Flexible manufacturing systems Genetic algorithms Job shop scheduling Manufacturing systems Optimal scheduling Processor scheduling Production systems scheduling Space exploration Transportation transportation systems Vehicles |
| title | Modeling strategy by adaptive genetic algorithm for production reactive scheduling with simultaneous use of machines and AGVs |
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