To produce the final product, parts need to be fabricated in the process stages and thereafter several parts are joined under the assembly operations based on the predefined bill of materials. But assembly relationship between the assembly parts and components has not been considered in general job shop scheduling problem model. The aim of this research is to find the schedule which minimizes completion time of Assembly Job Shop Scheduling Problem (AJSSP). Since the complexity of AJSSP is NP-hard, a hybrid particle swarm optimization (HPSO) algorithm integrated PSO with Artificial Immune is proposed and developed to solve AJSSP. The selection strategy based on antibody density makes the particles of HPSO maintain the diversity during the iterative process, thus overcoming the defect of premature convergence. Then HPSO algorithm is applied into a case study development from classical FT06. Finally, the effect of key parameters on the proposed algorithm is analyzed and discussed regarding how to select the parameters. The experiment result confirmed its practice and effectiveness.

The aquatic ecosystem of lake is a complex open systems, this study that is based on the principles of maximum flux selects Caohai as the object, we apply complex systems analysis to construct eutropication model which is suitable for local conditions and select a larger time scale and control parameters to simulate the eutrophication status to find out the main factors and to put forward reasonable proposals for giving out optimal control techniques. This model can be expanded in time scale, spatial scale and the number of parameters, while the difficulty of calculation and analysis will not be increased. The trend and influencing factors of the system will be got at the same time.

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The paper qualitatively and quantitatively analyzes a multi-state water supply subsystem in one fire protection system using stated analysis technique of GO Methodology. All states and probabilities of components are obtained, as well as minimal cut sets and the probabilities of them under failure states. The results prove to be effective for analyzing reliability of repairable systems with the stated analysis technique of GO methodology.