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dc.contributor.authorYang, S-
dc.identifier.citation2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, 2: 1200 - 1205, 12 Oct 2005en_US
dc.descriptionThis article is posted here with permission of IEEE - Copyright @ 2005 IEEEen_US
dc.description.abstractJob-shop scheduling is one of the most difficult production scheduling problems in industry. This paper presents an improved adaptive neural network together with heuristic methods for job-shop scheduling problems. The neural network is based on constraints satisfaction of job-shop scheduling and can adapt its structure and neuron connections during the solving. Several heuristics are also proposed to be combined with the neural network to guarantee its convergence, accelerate its solving process, and improve the quality of solutions. Experimental study shows that the proposed hybrid approach outperforms two classical heuristic algorithms regarding the quality of solutionsen_US
dc.subjectJob-shop schedulingen_US
dc.subjectAdaptive neural networken_US
dc.subjectConstraint satisfactionen_US
dc.titleAn improved adaptive neural network for job-shop schedulingen_US
dc.typeConference 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-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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