Job-shop scheduling with an adaptive neural network and local search hybrid approach
dc.cclicence | N/A | en |
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.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.funder | N/A | en |
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.doi | https://doi.org/10.1109/IJCNN.2006.247176 | |
dc.identifier.uri | http://hdl.handle.net/2086/13569 | |
dc.language.iso | en_US | en |
dc.peerreviewed | Yes | en |
dc.projectid | N/A | en |
dc.publisher | IEEE Press | en |
dc.researchgroup | Centre for Computational Intelligence | en |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | 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 |
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