Job-shop scheduling with an adaptive neural network and local search hybrid approach

dc.cclicenceN/Aen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2017-03-14T14:50:54Z
dc.date.available2017-03-14T14:50:54Z
dc.date.issued2006
dc.description.abstractJob-shop scheduling is one of the most difficult production scheduling problems in industry. This paper proposes an adaptive neural network and local search hybrid approach for the job-shop scheduling problem. The adaptive neural network is constructed based on constraint satisfactions of job-shop scheduling and can adapt its structure and neuron connections during the solving process. The neural network is used to solve feasible schedules for the job-shop scheduling problem while the local search scheme aims to improve the performance by searching the neighbourhood of a given feasible schedule. The experimental study validates the proposed hybrid approach for job-shop scheduling regarding the quality of solutions and the computing speed.en
dc.funderN/Aen
dc.identifier.citationYang, S. (2006) Job-shop scheduling with an adaptive neural network and local search hybrid approach. Proceedings of the 2006 IEEE Int. Joint Conf. on Neural Networks, pp. 2720-2727en
dc.identifier.doihttps://doi.org/10.1109/IJCNN.2006.247176
dc.identifier.urihttp://hdl.handle.net/2086/13569
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectidN/Aen
dc.publisherIEEE Pressen
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectJob-shop schedulingen
dc.subjectAdaptive neural networken
dc.titleJob-shop scheduling with an adaptive neural network and local search hybrid approachen
dc.typeConferenceen

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