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

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    Main article (176.4Kb)
    Date
    2001-08-09
    Author
    Yang, Shengxiang;
    Wang, Dingwei
    Metadata
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    Abstract
    A 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.
    Description
    Citation : Yang, 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. 955-971
    URI
    http://hdl.handle.net/2086/13445
    DOI
    http://dx.doi.org/10.1016/S0305-0548(00)00018-6
    Research Group : Centre for Computational Intelligence
    Research Institute : Institute of Artificial Intelligence (IAI)
    Peer Reviewed : Yes
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    • School of Computer Science and Informatics [2977]

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