Aspect-based sentiment analysis tries to estimate different emotional tendencies expressed in different aspects of a sentence. Aiming at the problem that the existing network model based on Recurrent Neural Network (RNN) combined with attention mechanism has too many training parameters and lacks explanation of related syntax constraints and long distance word dependence mechanism, a self-attention gated graph convolutional network was proposed, namely MSAGCN. First, the multi-headed self-attention mechanism was used to encode context words and targets, thus capturing semantic associations within the sentence. Then, a graph convolutional network was established on the sentence's dependency tree to obtain syntactic information and word dependencies. Finally, the sentiment of the specific target was obtained through the GTRU (Gated Tanh-ReLU Unit). Compared with the baseline model, the proposed model has the accuracy and F1 improved by 1%-3.3% and 1.4%-6.3% respectively. At the same time, the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model was also applied to the current task to further improve the model effect. Experimental results verify that the proposed model can better grasp the emotional tendencies of user reviews.

It is difficult to obtain the overall optimal solution by the traditional order batching and the picking path step-by-step optimization of picking operation in the logistics distribution center. In order to improve the efficiency of picking operation, a joint picking strategy based on nested genetic algorithm for order batching and path optimization was proposed. Firstly, the joint optimization model of order batching and picking path was established with the shortest total picking time as the objective function. Then, a nested genetic algorithm was designed to solve the model with the consideration of the complexity of double optimizations. The order batching result was continuously optimized in the outer layer, and the picking path was optimized in the inner layer according to the order batching result in the outer layer. Results of the examples show that, compared with the traditional strategies of order step-by-step optimization and step-by-step optimization in batches, the proposed strategy has reduced the picking time by 45.6% and 6% respectively, and the joint optimization model based on nested genetic algorithm results in shorter picking path and less picking time. To verify that the proposed algorithm has better performance on orders with different sizes, the simulation experiments were performed to the examples with 10, 20, 50 orders respectively. The results show that, with the increase of order quantity, the overall picking distance and time are further reduced, the decrease of picking time is risen from 6% to 7.2%.The joint optimization model of picking operation based on nested genetic algorithm and its solution algorithm can effectively solve the joint optimization problem of order batching and picking path, and provide the basis for the optimization of picking system in the distribution center.