Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/4817
Title: An improved constraint satisfaction adaptive neural network for job-shop scheduling
Authors: Yang, Shengxiang
Wang, Xingwei
Chai, Tianyou
Kendall, Graham
First Published: 21-Mar-2009
Publisher: Springer Verlag
Citation: Journal of Scheduling, 2009, 'Online First'.
Abstract: This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.
DOI Link: 10.1007/s10951-009-0106-z
ISSN: 1094-6136
Links: http://link.springer.com/article/10.1007%2Fs10951-009-0106-z
http://hdl.handle.net/2381/4817
Type: Article
Appears in Collections:Published Articles, Dept. of Computer Science

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