An improved constraint satisfaction adaptive neural network for job-shop scheduling
An improved constraint satisfaction adaptive neural network for job-shop scheduling
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DOI：
10.1007/s10951-009-0106-z
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发表时间：
2010-02
影响因子：
2
通讯作者：
Shengxiang Yang;Dingwei Wang;T. Chai;G. Kendall
中科院分区：
文献类型：
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作者：
Shengxiang Yang;Dingwei Wang;T. Chai;G. Kendall
针对车间作业调度问题，提出了一种改进的约束满足自适应神经网络。基于作业车间调度问题的约束条件构造了神经网络。它的结构和神经元连接可以根据求解过程中出现的实时约束满足情况自适应变化。在神经网络中还集成了几种启发式方法，以增强其收敛性，加快其收敛速度，并提高产生的解的质量。基于一组基准作业车间调度问题的实验研究表明，改进的约束满足自适应神经网络在计算时间和生成的调度质量方面都优于原约束满足自适应神经网络。神经网络方法也被实验验证优于三种经典的启发式算法，这些算法被广泛用作许多最先进的调度系统的基础。因此，它也可用于构建先进的作业车间调度系统。
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.