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Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems

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Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems

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dc.contributor.author Branco, Rogério M. es_ES
dc.contributor.author Coelho, Antônio S. es_ES
dc.contributor.author Mayerle, Sérgio F. es_ES
dc.date.accessioned 2016-11-16T14:14:07Z
dc.date.available 2016-11-16T14:14:07Z
dc.date.issued 2016-07-13
dc.identifier.issn 2340-5317
dc.identifier.uri http://hdl.handle.net/10251/74220
dc.description.abstract [EN] This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson’s algorithm, obtains a scheduling of the selected routes in a given planning horizon. The scheduler considers the preparation time between operations on the machines and can manage operations exchange respecting the route and the order given by the chromosome. The best results in the evolutionary process are individuals with routes and processing orders optimized for this type of problema. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València
dc.relation.ispartof International Journal of Production Management and Engineering
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Genetic algorithms es_ES
dc.subject Dispatching rules es_ES
dc.subject Realistic job-shop scheduling es_ES
dc.title Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems es_ES
dc.type Artículo es_ES
dc.date.updated 2016-11-16T13:57:14Z
dc.identifier.doi 10.4995/ijpme.2016.5780
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Branco, RM.; Coelho, AS.; Mayerle, SF. (2016). Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems. International Journal of Production Management and Engineering. 4(2):75-85. doi:10.4995/ijpme.2016.5780. es_ES
dc.relation.publisherversion https://doi.org/10.4995/ijpme.2016.5780 es_ES
dc.description.upvformatpinicio 75 es_ES
dc.description.upvformatpfin 85 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 4
dc.description.issue 2
dc.identifier.eissn 2340-4876


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