Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5862
Title: An improved adaptive neural network for job-shop scheduling
Authors: Yang, S
Keywords: Job-shop scheduling;Adaptive neural network;Constraint satisfaction;Heuristics
Issue Date: 2005
Publisher: IEEE
Citation: 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, 2: 1200 - 1205, 12 Oct 2005
Abstract: Job-shop scheduling is one of the most difficult production scheduling problems in industry. This paper presents an improved adaptive neural network together with heuristic methods for job-shop scheduling problems. The neural network is based on constraints satisfaction of job-shop scheduling and can adapt its structure and neuron connections during the solving. Several heuristics are also proposed to be combined with the neural network to guarantee its convergence, accelerate its solving process, and improve the quality of solutions. Experimental study shows that the proposed hybrid approach outperforms two classical heuristic algorithms regarding the quality of solutions
Description: This article is posted here with permission of IEEE - Copyright @ 2005 IEEE
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1571309
http://bura.brunel.ac.uk/handle/2438/5862
DOI: http://dx.doi.org/10.1109/ICSMC.2005.1571309
ISBN: 0-7803-9298-1
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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