Due to the complex and variable structure of fundus vessels， and the low contrast between the fundus vessel and the background， there are huge difficulties in segmentation of fundus vessels， especially small fundus vessels. U-Net based on deep fully convolutional neural network can effectively extract the global and local information of fundus vessel images，but its output is grayscale image binarized by a hard threshold， which will cause the loss of vessel area， too thin vessel and other problems. To solve these problems， U-Net and Pulse Coupled Neural Network （PCNN） were combined to give play to their respective advantages and design a fundus vessel segmentation method. First， the iterative U-Net model was used to highlight the vessels， the fusion results of the features extracted by the U-Net model and the original image were input again into the improved U-Net model to enhance the vessel image. Then， the U-Net output result was viewed as a gray image， and the PCNN with adaptive threshold was utilized to perform accurate vessel segmentation. The experimental results show that the AUC （Area Under the Curve） of the proposed method was 0.979 6，0.980 9 and 0.982 7 on the DRVIE， STARE and CHASE_DB1 datasets， respectively. The method can extract more vessel details， and has strong generalization ability and good application prospects.

Concerning the problem that the manual design of airport arrival procedures is time consuming and it is difficult to optimize the path length quantitatively， a three-dimensional automatic optimization design method of multiple arrival procedures was proposed. Firstly， based on the specifications of RNAV （Rules for implementation of area NAVigation）， the geometric configuration and the merging structure of the arrival procedures were modeled. Then， considering airport layout and aircraft operation constraints such as obstacle avoidance and route separation， with the goal of minimizing the total length of arrival procedures， a complete mathematical model was established. Finally， a hybrid algorithm based on simulated annealing algorithm and improved A^* algorithm was developed to automatically optimize the merging structure of arrival procedures. Simulation results show that， in the experiment based on Sweden Arlanda Airport， compared with the existing related integer programming method， the hybrid simulated annealing algorithm can shorten the total path length by 3% and reduce the computing time by 87%. In the experiment based on Shanghai Pudong Airport， compared with the actual arrival procedures， the length of the routes designed by the proposed algorithm is reduced by 6.6%. These results indicate that the proposed algorithm can effectively design multiple three-dimensional arrival procedures， and can provide preliminary decision support for the procedure designers.

From the perspective of revenue management, a new approach for order acceptance under uncertainty in Make-to-Oder (MTO) manufacturing using average-reward reinforcement learning was proposed. In order to maximize the average expected revenue, the proposed approach took order types and different combinations of price and leadtime as criteria for the classification of the system states based on multi-level pricing mechanism. The simulation results show that the proposed algorithm has learning and selective ability to accept the order. Comparisons made with other order acceptance policies show the effectiveness of the proposed algorithm in average revenue, accepted order types, and adaptability.