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Fast local search for fuzzy job shop scheduling

dc.contributor.authorPuente Peinador, Jorge 
dc.contributor.authorRodríguez Vela, María del Camino 
dc.contributor.authorGonzález Rodríguez, Inés
dc.identifier.citationFrontiers in Artificial Intelligence and Applications, 215, p. 739-744 (2010); doi:10.3233/978-1-60750-606-5-739spa
dc.descriptionECAI 2010
dc.description.abstractIn the sequel, we propose a new neighbourhood structure for local search for the fuzzy job shop scheduling problem. This is a variant of the well-known job shop problem, with uncertainty in task durations modelled using fuzzy numbers and where the goal is to minimise the expected makespan of the resulting schedule. The new neighbourhood structure is based in changing the relative order of subsequences of tasks within critical blocks. We study its theoretical properties and provide a makespan estimate which allows to select only feasible neighbours while covering a greater portion of the search space than a previous neighbourhood from the literature. Despite its larger search domain, experimental results show that this new structure notably reduces the computational load of local search with respect to the previous neighbourhood while maintaining or even improving solution quality
dc.format.extentp. 739-744spa
dc.publisherIOS Press
dc.relation.ispartofFrontiers in Artificial Intelligence and Applicationsspa
dc.rights© 2010 Los autores con licencia Ios Press
dc.rightsCC Reconocimiento - No comercial - Sin obras derivadas 4.0 Internacional
dc.titleFast local search for fuzzy job shop schedulingspa

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© 2010 Los autores con licencia Ios Press
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