Please use this identifier to cite or link to this item: http://buratest.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

Files in This Item:
File Description SizeFormat 
Fulltext.pdf149.48 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.