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

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dc.contributor.author Yang, Shengxiang en
dc.date.accessioned 2017-03-14T14:50:54Z
dc.date.available 2017-03-14T14:50:54Z
dc.date.issued 2006
dc.identifier.citation Yang, 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. 2720-2727 en
dc.identifier.uri http://hdl.handle.net/2086/13569
dc.description.abstract Job-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.language.iso en_US en
dc.publisher IEEE Press en
dc.subject Job-shop scheduling en
dc.subject Adaptive neural network en
dc.title Job-shop scheduling with an adaptive neural network and local search hybrid approach en
dc.type Conference en
dc.researchgroup Centre for Computational Intelligence en
dc.peerreviewed Yes en
dc.funder N/A en
dc.projectid N/A en
dc.cclicence N/A en


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