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dc.contributor.authorVaquerizo, M. Belén
dc.contributor.authorBaruque, Bruno
dc.contributor.authorCorchado Rodríguez, Emilio Santiago 
dc.date.accessioned2017-09-06T09:14:22Z
dc.date.available2017-09-06T09:14:22Z
dc.date.issued2012
dc.identifier.citationHybrid Artificial Intelligent Systems Lecture Notes in Computer Science. 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012. Proceedings, Part II. Lecture Notes in Computer Science. Volumen 7209, pp. 381-391.
dc.identifier.isbn978-3-642-28930-9 (Print) / 978-3-642-28931-6 (Online)
dc.identifier.issn0302-9743 (Print) / 1611-3349 (Online)
dc.identifier.urihttp://hdl.handle.net/10366/134870
dc.description.abstractThe Labour Scheduling problem in the context of any transport company is a complex optimization problem of that belongs to the class of NP-Hard problems. In these cases, it is not viable to try to find an exact solution and therefore, they require methods that assure the optimal management of the available resources in the tracing of the work calendars under the most suitable criteria of economy of times and costs. The main purpose of this research is to propose an efficient method to determine optimal shifts in a generic transport company, using bio-inspired methods. This method employs a two-step approach to obtain a solution. In a first stage, a Grasp algorithm is used to generate a viable solution. Then in a second stage, this preliminary solution is tuned, in order to obtain an optimal one, by using a Scatter Search algorithm.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectComputer Science
dc.titleCombining Metaheuristic Algorithms to Solve a Scheduling Problem
dc.typeinfo:eu-repo/semantics/article
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess


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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported