A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

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dc.cclicenceN/Aen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorWang, Dingweien
dc.date.accessioned2017-03-07T11:28:10Z
dc.date.available2017-03-07T11:28:10Z
dc.date.issued2001-08-09
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
dc.funderNational Natural Science Foundation of China (NSFC)en
dc.identifier.citationYang, S. and Wang, D. (2001) A new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Computers and Operations Research, 28 (10), pp. 955-971en
dc.identifier.doihttps://doi.org/10.1016/S0305-0548(00)00018-6
dc.identifier.urihttp://hdl.handle.net/2086/13445
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid69684005en
dc.publisherElsevieren
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectJob-shop schedulingen
dc.subjectAdaptive neural networken
dc.subjectHeuristicsen
dc.titleA new adaptive neural network and heuristics hybrid approach for job-shop schedulingen
dc.typeArticleen

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