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dc.contributor.authorYang, Shengxiangen
dc.contributor.authorWang, Dingweien
dc.date.accessioned2017-03-23T10:41:50Z
dc.date.available2017-03-23T10:41:50Z
dc.date.issued1999
dc.identifier.citationYang, S. and Wang, D. (1999) Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling. Proceedings of the 14th IFAC World Congress, Vol. J: Discrete Event Systems, Stochastic Systems, Fuzzy and Neural Systems I, pp. 175-180en
dc.identifier.urihttp://hdl.handle.net/2086/13829
dc.description.abstractAn efficient constraint satisfaction based adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The adaptive neural network has the property of adaptively adjusting its connection weights and biases of neural units according to the sequence and resource constraints of job-shop scheduling problem while solving feasible solution. Two heuristics are used in the hybrid approach: one is used to accelerate the solving process of neural network and guarantee its convergence, the other is used to obtain non-delay schedule from solved feasible solution by neural solution by neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and excellent efficiency.en
dc.language.isoen_USen
dc.publisherElsevier Science Ltden
dc.subjectJob-shop schedulingen
dc.subjectConstraint satisfactionen
dc.subjectNeural networksen
dc.subjectHeuristicsen
dc.titleConstraint satisfaction adaptive neural network and efficient heuristics for job-shop schedulingen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Nature Science Foundation of Chinaen
dc.funderNational High-Tech Program of Chinaen
dc.projectid69684005en
dc.projectid863-511-9609-003en
dc.cclicenceCC-BY-NCen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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