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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorWang, Dingweien
dc.contributor.authorChai, Tianyouen
dc.contributor.authorKendall, Grahamen
dc.date.accessioned2013-05-16T09:06:09Z
dc.date.available2013-05-16T09:06:09Z
dc.date.issued2010
dc.identifier.citationYang, S. et al. (2010) An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of Scheduling, 13(1), February 2010, pp. 17-38.en
dc.identifier.issn1094-6136
dc.identifier.urihttp://hdl.handle.net/2086/8514
dc.description.abstractThis paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.en
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.subjectJob-shop schedulingen
dc.subjectConstraint satisfaction adaptive neural networken
dc.subjectHeuristicsen
dc.subjectActive scheduleen
dc.subjectNon-delay scheduleen
dc.subjectPriority ruleen
dc.subjectComputational complexityen
dc.titleAn improved constraint satisfaction adaptive neural network for job-shop scheduling.en
dc.typeArticleen
dc.identifier.doihttp://dx.doi.org/10.1007/s10951-009-0106-z
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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