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|Title:||Job-Shop Scheduling with an Adaptive Neural Network and Local Search Hybrid Approach|
|Publisher:||Institute of Electrical and Electronics Engineers (IEEE)|
|Citation:||IEEE International Joint Conference on Neural Networks 2006, Conference Proceedings, pp. 2720-2727.|
|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 is the author's final draft of the paper published as IEEE International Joint Conference on Neural Networks 2006, Conference Proceedings, pp. 2720-2727. The final version is available from http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=36115&arnumber=1716466&count=787&index=404&tag=1. Copyright © 2006 IEEE. Doi: 10.1109/IJCNN.2006.247176 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Leicester’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to firstname.lastname@example.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.|
|Appears in Collections:||Conference Papers & Presentations, Dept. of Computer Science|
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