Deep reinforcement learning algorithm for solving material emergency dispatching problem

In order to solve the problem that the scheduling scheme cannot be updated in real time due to the dynamic change of node demand in material emergency dispatching, this article proposes a dynamic attention model based on improved gated recurrent unit. The dynamic codec framework is used to track the change of node demand to update the node information. The improved gated recurrent unit is embedded between codecs to improve the representation ability of the model. By weighted combination of the node information of the previous time, the current time and the initial time, a more representative node embedding is obtained. The results show that compared with the elitism-based immigrants ant colony optimization algorithm, the solution quality of the proposed model was improved by 27.89, 27.94, 28.09 and 28.12% when the problem scale is 10, 20, 50 and 100, respectively, which can effectively deal with the instability caused by the change of node demand, so as to minimize the cost of material distribution.