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dc.contributor.authorYang, S-
dc.contributor.authorWang, D-
dc.identifier.citationIEEE Transactions on Neural Networks, 11(2): 474 - 486, Mar 2000en_US
dc.descriptionCopyright @ 2000 IEEEen_US
dc.description.abstractThis paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.en_US
dc.description.sponsorshipThis work was supported by the Chinese National Natural Science Foundation under Grant 69684005 and the Chinese National High-Tech Program under Grant 863-511-9609-003, the EPSRC under Grant GR/L81468.en_US
dc.subjectAdaptive neural networken_US
dc.subjectConstraint satisfactionen_US
dc.subjectGeneralized job-shop scheduling problemen_US
dc.titleConstraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop schedulingen_US
dc.typeResearch Paperen_US
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
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Computer Science
Dept of Computer Science Research Papers

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