基于模拟退火Hopfield网络模型的有约束FMS资源调度

Resource Constrained Scheduling for FMS Based on Simulated Annealing Hopfield Neural Network

介绍了利用神经网络和模拟退火技术来求解有约束的ＦＭＳ资源调度问题的方法，有约束的ＦＭＳ资源调度问题首先被分解为一系列时间间隔的调度，这些时间间隔的调度由事件驱动，随着这些时间间隔的调度的完成，整个调度过程结束。仿真结果表明，由于这种方法是梯度下降法和随机搜索法的综合，因而它可以克服通常神经网络容易陷入局部极小点的缺点。

An approach for resource constrained FMS scheduling is described which integrates Hopfield neural network and simulated annealing in this paper.The resource constrained FMS scheduling problem is first decomposed into a series interval scheduling which is activated by accident.The whole scheduling is completed with a series interval scheduling and the whole scheduling is fulfilled.The simulated results show that the approach can dislodge a state from a local minimum and guide it to the global minimum