摘要
本文介绍了用神经网络求解ＦＭＳ中有约束的资源调度问题的方法。有约束的资源调度问题首先被分解成一系列多维背包模型并且为背包模型建立了一个等价的Ｈｏｐｆｉｅｌｄ神经网络，然后通过扩展Ｈｏｐｆｉｅｌｄ网络，给出了一种求解有约束的资源调度问题的方法。这种方法可以避免通常神经网络所具有的不稳定性和容易陷入局部极小点的缺陷。
In this paper,a neural network method used to solve the resource constrained schedulingproblem in FMS is proposed. The resource constrained scheduling problem is first decomposed into a series ofmultidimensional knapsack models and an equivalent Hopfield network model for this representation is estab-lished. Then, by extending Hopfield network,an approach to solve the resource constrained scheduling prob-lem is developed.This approach can avoid such common neural network difficulties as instability and localminima.
出处
《信息与控制》
CSCD
北大核心
1995年第5期305-311,共7页
Information and Control
关键词
神经网络
约束
资源调度
柔性制造系统
neural network, constrain, resource scheduling, multidimensional knapsack model, FMS
