基于神经网络和知识库的物料配送动态调度

Dynamic Scheduling of Material Delivery Based on Neural Network and Knowledge Base

为有效地解决汽车混流装配线中多载量小车物料配送的动态调度问题,提出基于知识库和神经网络的调度方法.首先,对汽车装配线物料配送的动态调度问题进行描述,建立以装配线产量和多载量小车的物料搬运距离作为衡量指标的目标函数.然后通过Plant Simulation软件生成针对汽车混流装配线的仿真数据并对神经网络模型进行离线训练,在实时阶段利用神经网络模型和知识库实现多载量小车最优调度规则的选取.实验结果表明:所提出的调度规则选取方法选择的调度规则大多为最优调度规则,以较低的调度规则计算复杂性确保了调度的实时性能,能够很好地应对动态环境的变化,从而有效提升了多载量小车的动态调度水平.

In order to tackle the dynamic scheduling problem of tow trains in mixed-model assembly lines,a scheduling approach is proposed based on the knowledge base and neural network.Firstly,the dynamic scheduling problem of material delivery in the automotive assembly line is formally described.The throughput of the assembly line and the total delivery distances are selected as components of the objective function.After that,the sample data of mixed-model assembly lines are generated by the Plant Simulation software and are used to train the neural network model offline.Finally,the trained neural network model and the knowledge base are adopted in the real-time scheduling process to select the optimal scheduling rule for tow trains.The experimental results indicate that the scheduling rules selected by the selection method proposed in the paper are mostly the optimal ones.The lower computational complexity of scheduling rules ensures the real-time performance of scheduling.It can cope well with changes in the dynamic environment,thus effectively improving the dynamic scheduling of tow trains.