Research On Multi-Project Scheduling Based On Particle Swarm Optimization And Critical Chain Technologies

This paper, based on elaborating the traditional project schedule planning methods, analyzes their main advantages and disadvantages, and highlights the significant advantages of critical chain project management method in contrast with traditional project management methods on many aspects, which especially are that the critical chain method can effectively manage the uncertainty of project, shorten project cycle and improve project efficiency. The critical chain method, fully considering the factor of people's subjective behaviors, is therefore more practical than traditional methods.Critical chain identification is an important basis for critical chain project management in the study of critical chain technologies. However, the majority of existing critical chain identification methods doesn't take the randomness of project activities duration into account effectively. Therefore, this paper proposes a critical chain identification method on basis of statistics theory. Experiments show that this method works well and can control the project duration while taking into account the project practicality.Buffer management is an effective way among the critical chain technologies to cope with uncertainty, but the existing buffer size determination methods don't fully consider the characteristics of various activities in project. Thus this paper proposes a self-adaptive method for setting buffer. Experiments show that this method can effectively reduce the buffer size imported in the project and can effectively avoids new resource conflicts generated by setting buffer.On the basis of the above, this paper conducts a research and analysis of limit situation of multi-project schedule management, introduces critical chain theory to multi-project planning and schedule management, then establishes a critical chain method-based multi-project planning and scheduling model and at last proposes the corresponding objective function.To solve the objective function, this paper, based on Particle Swarm Optimization (PSO), designs a PSO algorithm mixed with genetic manipulation, which uses a new particle coding mode. This coding mode takes random priority and delay time as gene particles. The randomness of each gene ensures initial population a homogeneous distribution within feasible solution space and the genetic information this gene carries ensures that sub-project priority value, which makes the objective function the shortest, can be found and be hereditary in the subsequent algorithm process. After each iteration, outstanding individuals will be deposited into the memory, while new individuals will be generated randomly and become a member of new populations. On one hand, these newly-generated individuals can maintain population diversity and at the same time reduce the possibility of premature convergence of the algorithm; on the other hand, the memory information can be used to maintain the overall quality of population.To test the effectiveness of the algorithm, this paper puts forward a multi-project instances generation method, which selects existing single-project instances from the standard PSPLIB to generate necessary multi-project instances in accordance with the given parameters. Then, this paper uses the algorithm mentioned above to conduct a simulating calculation for these generated multi-project instances and further illustrates the effectiveness of the algorithm by analyzing and comparing the simulating calculation result.At last, this paper puts forward the major problems in the project planning management of a large air conditioner manufacturer, combined with analysis of planning and scheduling problems in this manufacturer. At the same time, the proposed multi-project schedule management method is applied to the actual production process of the manufacturer, which obtains a good result.