Research On Project Scheduling With Uncertain Environment

The rapid development of economy and technology rise to the continuous expansion of production scale, which causes the role of project scheduling process also growing throughout the project management. A good scheduling scheme can make the entire project management more efficient. In general, the main factors which affect the project schedule include the cycle of activity, the amount of available resources and so on. And these factors are often uncertain in real life. Therefore, project scheduling with uncertainty environment is arisen from the question.For the above-mentioned problems, this thesis mainly concentrates on the uncertain activity cycle and available resources project existing in the project scheduling. Two solutions based on single objective and a solutions based on multi-objective are proposed.Firstly, fuzzy theory is introduced in this paper. Uncertain factors of project scheduling problem can be described by fuzzy theory. In this thesis, uncertain cycle of activity is described by the triangular membership function, and the uncertain amount of available resources is described and by trapezoid membership function. Then, the corresponding mathematical model is given.Secondly, this study first heuristic algorithm to project scheduling with uncertainty environment solved. In the heuristic algorithm used in this paper is the first serial schedule generation mechanism and end time of the priority rule method to generate an executable sequence.Again, in order to avoid situation that the heuristic algorithm often lead to local optimal solution of the problems, this study also proposed based on genetic algorithm, simulated annealing algorithm with the combined method of hybrid genetic algorithm model for solving the problem. The hybrid genetic algorithm overcomes shortcomings of genetic algorithm own, and has achieved good result, at the same time in practice a better application.Finally, multi-objective scheduling problems with the uncertain environment studied. Using the improved NSGA-II algorithm to the problem was solved in the traditional algorithm is introduced based on adaptive crossover and adaptive mutation operator. Solutions and results obtained using simulation of fire and further refine the algorithm. The algorithm achieved fairly good results by application in real life.