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http://bura.brunel.ac.uk/handle/2438/5892
Title: | Constraint satisfaction adaptive neural network and efficient heuristics for job-shop scheduling |
Authors: | Yang, S Wang, D |
Keywords: | Job-shop scheduling;Constraint satisfaction;Neural networks;Heuristics |
Issue Date: | 1999 |
Publisher: | IFAC |
Citation: | 14th IFAC World Congress, Beijing, China, Journal of Discrete Event Systems, Stochastic Systems, Fuzzy and Neural Systems I: 175 - 180, 05 - 09 Jul 1999 |
Abstract: | An 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 adatptively 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. |
Description: | Copyright @ 1999 IFAC |
URI: | http://bura.brunel.ac.uk/handle/2438/5892 |
ISBN: | 0080432212 978-0080432212 |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
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