Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5970
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dc.contributor.authorYang, S-
dc.contributor.authorWang, D-
dc.date.accessioned2011-11-21T11:11:20Z-
dc.date.available2011-11-21T11:11:20Z-
dc.date.issued2001-
dc.identifier.citationComputers and Operations Research, 28(10): 955 - 971, Sep 2001en_US
dc.identifier.issn0305-0548-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0305054800000186en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/5970-
dc.descriptionCopyright @ 2001 Elsevier Science Ltden_US
dc.description.abstractA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.en_US
dc.description.sponsorshipThis work is supported by the National Nature Science Foundation (No. 69684005) and National High -Tech Program of P. R. China (No. 863-511-9609-003).en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectJob-shop schedulingen_US
dc.subjectAdaptive neural networken_US
dc.subjectHeuristicsen_US
dc.titleA new adaptive neural network and heuristics hybrid approach for job-shop schedulingen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/S0305-0548(00)00018-6-
pubs.organisational-data/Brunel-
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-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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