嵌入指针网络的深度循环神经网络模型求解作业车间调度问题

提出了一种数据驱动的作业车间调度算法,训练样本来源于基准实例和部分实际生产数据,通过特征函数来构建样本的特征数据并进行归一化处理,标签数据由调度任务和相应的调度规则的映射关系构成,以LSTM模型为主框架,在模型中嵌入指针网络,将当前序列中概率最大的工件优先进入缓冲区,提高了神经网络的训练速度和质量,采用训练后的模型对新问题进行求解。结果证明了所构建模型的有效性,同时为求解作业车间调度问题提供了新思路。

Method to solve Job-Shop scheduling problem using deep recurrent neural network model with embedded pointer network

This paper proposed a data-driven Job-Shop scheduling algorithm.It derived the training samples from some benchmark instances and actual production data.It constructed the feature data of the samples using the feature function and then normalized.It constituted the tag data by the mapping relations between the scheduling tasks and the corresponding scheduling rules.This paper embedded a pointer network into the main framework of the LSTM recurrent neural network model so that the workpiece with the highest probability in the current sequence would be passed to the buffer at first,which improved the training speed and training quality of the neural network.The result of an experiment shows that the proposed model is effective in solving Job-Shop scheduling problem after training.This study provides a new idea for solving the Job-Shop scheduling problem.