Coping with the characteristic of flow shop scheduling problem with uncertain due date, fuzzy arithmetic on fuzzy numbers is applied to describe the problem, and then a new hybrid algorithm model which integrate particle swarm optimization into the evolutionary mechanism of the knowledge evolution algorithm is presented to solve the problem. By the evolutionary mechanism of knowledge evolution algorithm, we can exploit the global search ability. By the operating characteristic of PSO, we can enhance the local search ability. The algorithm is tested with MATLAB simulation. The result, compared with Genetic algorithm and modified particle swarm optimization, shows the feasibility and effectiveness of the proposed algorithm.

Flexible Job-shop Scheduling Problem is the extending of the classical Job-shop Scheduling Problem, which has more practical significance than JSP. This paper firstly presents a solution for FJSP under uncertainty based on QPSO algorithm, mainly on uncertain operation time and uncertain delivery time, and then describes the mathematical model and solving process. Subsequent sections concentrate on the designed and conducted experiment simulation using instances, and analyze the experimental results on the QPSO performance compared with some results of other traditional algorithms from literature review. Finally, this paper illustrates QPSO has better performance and shows the promising for dealing with FJSP.

Initial rainwater pollution is an important non-point source pollution of urban drainage systems. The application of storage tanks and other reduction facilities has play a key role in reducing first flush pollution. Because of the lack of scientific evaluation system, the evaluation is disorderly and there are many operational problems. This case is based on the reduction facilities research of Suzhou Creek in Shanghai, through which a standard evaluation system and implementation procedure are built to perfect the incomprehensive and unscientific system, so that the efficiency can be improved.

Production scheduling problem is the one of the most basic, important and difficult theoretical research in a manufacturing system. In the past decades of research, the classical scheduling theory has made signficant progress, but the actual scheduling problems are much more complicated than the classical theory. In order to study, the actual scheduling problems are often made much simpler. Therefore, it is difficult for the results of the classical scheduling theory to been put into practice. The considerable gap also exists between the classical scheduling theory and the actual scheduling problem. But with the increasingly fierce market competition, customers have become increasingly demanding product diversification, product life cycle is shorter and more sophisticated structure of the product makes the actual scheduling a large number of uncertainties, the traditional model has become difficult to obtain satisfactory results. This paper makes a analysis on the uncertainties of production scheduling,and tries to solve re-entrant production scheduling problem based on QPSO. It introduces the mathematical model and the solving process based on QPSO algorithm. Then it makes construction strategies to solve it. The paper simulates with Visual C. The results show that this algorithm is feasibility.