For the Flexible Job-shop Scheduling Problem (FJSP) with machine flexibility and machine tool depreciation, in order to reduce the energy consumption in the production process, a mathematical model with the minimization of weighted sum of maximum completion time and total energy consumption as the scheduling objective was established, and an Improved Genetic Algorithm (IGA) was proposed. Firstly, according to strong randomness of Genetic Algorithm (GA), the principle of balanced dispersion of orthogonal test was introduced to generate initial population, which was used to improve the search performance in global range. Secondly, in order to overcome genetic conflict after crossover operation, the coding mode of three-dimensional real numbers and the arithmetic crossover of double individuals were used for chromosome crossover, which reduced the steps of conflict detection and improved the solving speed. Finally, the dynamic step length was adopted to perform genetic mutation in mutation operation stage, which guaranteed local search ability in global range. By testing on the 8 Brandimarte examples and comparing with 3 improved heuristic algorithms in recent years, the calculation results show that the proposed algorithm is effective and feasible to solve the FJSP.

Focused on the problem of traditional object recognition algorithm that the artificially designed features were more susceptible to diversity of object shapes, illumination and background, a deep convolutional neural network algorithm was proposed for object recognition. Firstly, this algorithm was trained with NYU Depth V2 dataset, and single depth information was transformed into three channels. Then color images and transformed depth images in the training set were used to fine-tune two deep convolutional neural networks, respectively. Next, color and depth image features were extracted from the first fully connected layers of the two trained models, and the two features from the resampling training set were combined to train a Linear Support Vector Machine (LinSVM) classifier. Finally, the proposed object recognition algorithm was used to extract super-pixel features in scene understanding task. The proposed method can achieve a classification accuracy of 91.4% on the test set which is 4.1 percentage points higher than SAE-RNN (Sparse Auto-Encoder with the Recursive Neural Networks). The experimental results show that the proposed method is effective in extracting color and depth image features, and can effectively improve classification accuracy.