Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5970
Title: A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
Authors: Yang, S
Wang, D
Keywords: Job-shop scheduling;Adaptive neural network;Heuristics
Issue Date: 2001
Publisher: Elsevier
Citation: Computers and Operations Research, 28(10): 955 - 971, Sep 2001
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: Copyright @ 2001 Elsevier Science Ltd
URI: http://www.sciencedirect.com/science/article/pii/S0305054800000186
http://bura.brunel.ac.uk/handle/2438/5970
DOI: http://dx.doi.org/10.1016/S0305-0548(00)00018-6
ISSN: 0305-0548
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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