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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 |
Issue 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: | Publications Computer Science Dept of Computer Science Research Papers |
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