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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5847

Title: Job-shop scheduling with an adaptive neural network and local search hybrid approach
Authors: Yang, S
Keywords: Adaptive scheduling
Adaptive systems
Computer science
Constraint optimization
Job production systems
Job shop scheduling
Neural networks
Neurons
Processor scheduling
Sorting
Publication Date: 2006
Publisher: IEEE
Citation: IEEE International Joint Conference on Neural Networks (IJCNN 2006): 2720 - 2727, 16-21 Jul 2006
Abstract: Job-shop scheduling is one of the most difficult production scheduling problems in industry. This paper proposes an adaptive neural network and local search hybrid approach for the job-shop scheduling problem. The adaptive neural network is constructed based on constraint satisfactions of job-shop scheduling and can adapt its structure and neuron connections during the solving process. The neural network is used to solve feasible schedules for the job-shop scheduling problem while the local search scheme aims to improve the performance by searching the neighbourhood of a given feasible schedule. The experimental study validates the proposed hybrid approach for job-shop scheduling regarding the quality of solutions and the computing speed.
Description: This article is posted here with permission from IEEE - Copyright @ 2006 IEEE
URI: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1716466
http://bura.brunel.ac.uk/handle/2438/5847
DOI: http://dx.doi.org/10.1109/IJCNN.2006.247176
ISBN: 0-7803-9490-9
Appears in Collections:Information Systems and Computing
School of Information Systems, Computing and Mathematics Research Papers
Publications

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