Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5766
Title: Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling
Authors: Yang, S
Wang, D
Keywords: Adaptive neural network;Constraint satisfaction;Generalized job-shop scheduling problem;Heuristic
Issue Date: 2000
Publisher: IEEE
Citation: IEEE Transactions on Neural Networks, 11(2): 474 - 486, Mar 2000
Abstract: This paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.
Description: Copyright @ 2000 IEEE
URI: http://bura.brunel.ac.uk/handle/2438/5766
DOI: http://dx.doi.org/10.1109/72.839016
ISSN: 1045–9227
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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