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Job shop scheduling is a key part of production management and control for manufacturing enterprises. An optimized scheduling is helpful for enterprise to strengthen its efficiency and competition. And particle swarm optimization is a young algorithm of swarm intelligence. So application and research of job shop scheduling based on particle swarm optimization has important practical significance. This paper analyze and diagnose the scheduling status of a mold manufacturing workshop, taking minimize make span and average of AI based on fuzzy processing-time and delivery as optimizing target, model the scheduling for the manufacturing of CQD-035. Eventually, programming on the platform of MATLAB7.0.1 using the discrete particle swarm algorithm, a satisfactory scheduling scheme is obtained.

The flow model was improved on, and the two kinds of circumstances were discussed. When the current flow point is on the borders, the flow direction is remained if the water doesn’t flow along the borders. When the current flow point is not next to the blank spots in two adjacent directions, the flow direction is remained. And then, I designed the algorithm of the follow-up processing steps to extract drainage-basin, such as process depression, the determination of river, river classification, partition side-area, search drainage- basin border, smooth river and smooth drainage-basin border. Finally, I solved the problems about the around-flows and the cross-flows. The input data contains the terrain elevation of the grid data, and the output data is MAPGIS open data.

In this paper, the compound control algorithm of fuzzy and PID has been applied in Acceleration Slip Regulation for vehicle to prevent the excessive spin of driving wheels on wet or icy roads. Simulation experiments have been done to investigate the performance of Acceleration Slip Regulation. The results show that the provided Fuzzy PID controller can effectively keep the slip ratio of the driving wheels in the optimal range and improve the driving ability of the vehicle.