An improved constraint satisfaction adaptive neural network for job-shop scheduling |
| |
Authors: | Shengxiang Yang Dingwei Wang Tianyou Chai Graham Kendall |
| |
Affiliation: | 1.Department of Computer Science,University of Leicester,Leicester,UK;2.Key Laboratory of Integrated Automation of Process Industry (Northeastern University), Ministry of Education,Northeastern University,Shenyang,China;3.School of Computer Science,University of Nottingham,Nottingham,UK |
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|