Partical Swarm Optimization And Grey Forecast Research Based On Quality Earned Value Integrated Management

Since the beginning of the 20th century, people had been searching scientific methods for the project management. Then the project management discipline has become more sohpisticated and popular, and been widely applied into production management process. The relevances among elements in modern project are becoming more close, and the requirements of integrated project management are becoming more high. Integrated project management has become the key factor for project success, and become the most important scientific project management in a comprehensive management. The main target of project management is through appropriate planning and controling to make each implementation activity of project achieving the best results, lowest costs, most great progress and expected benefit of the project, and make the company obtaining the maximum profit. It's an important way for achieveing the goal to carry out effective control for quality, schedule and cost. Single factor analysis method is not enough to the project process and performance guidance. Later, earned value management methods integrated management of the schedule and cost appeared in the project management and achieved true dynamic control.Earned value project management is the most effective method in an integrated controlling. It overcomes various deviations caused by the isolation between the control of cost and schedule. It makes them to link each other. But this method lacks monitoring and dynamic analysis on project quality. Correlation analysis on the questions mentioned above is made in this research. In order to slove the questions, quality earned value management methods is introduced, and the fuzzy comprehensive evaluation method is used to determine quality index. Because particle swarm optimization (PSO) algorithm tends to global search in early stage and optimal regions need to strengthen local search in later stage of the computation process, the dynamic inertia weight and accelerated factors applied to the optimization problem of quality earned value management are introduced into the classic particle swarm optimization algorithm. Aiming at low accuracy of GM(1,1) grey prediction model, an intention prediction expression is applied in particle swarm optimization algorithm to assure the effective forecast for the engineering project.