Research of Task Scheduling Problem in Product Data Management

An optimizing model for task scheduling problem in product data management (PDM) is developed to minimize the duration of the project, number of split and total interrupted time of tasks. A hybrid genetic algorithm based on greedy approach is developed to solve this model. Priority rule is applied in coding scheme of the chromosome and greedy criterion is adopted in decoding rule to process assembled task units. Neighborhood search approach is integrated into mutation operator of the individual to improve the mutation efficiency. Implementation approach of the algorithm is described. The effectiveness of this model and algorithm is verified by the simulation results