| The coordination among multiple objectives of engineering projects is essential in project management, directly influencing the achievement of goals of time, cost and quality. Nowadays, Engineering projects are becoming huge in investment scale, long in period and filled with risks, having more strict requirements in the management of coordination among multiple objectives. Therefore, Analyzing and optimizing multiple objectives of engineering projects, is the basis of realizing the balance of the objectives. Considering comprehensively the risk factors faced and the dynamic of the environment when optimizing, can make the optimization more consistent with the actual situation, providing theoretical basis to the practical management.This dissertation focused on the multi-objective optimization of engineering projects, based upon traditional theories of multi-objective optimization, using Fuzzy Set Theory, Utility Theory, Dynamic Optimization Theory, showing the impact of risk factors and dynamic environmental factors by fuzzifying parameters, selecting confidence level, evaluating multi-attribute utility function and bringing in the time parameter, then built a common model considering risk factors and dynamic environmental factors. After that, a time-cost trade-off function and time-quality tradeoff function were put forward by analyzing the characteristics of objectives and the relations among them. A dynamic fuzzy multi-objective optimization model of engineering projects considering risk factors was built, by combining with the common model. A kind of Particle Swarm Optimization algorithm which is suitable for dynamic optimization problem was selected to solve the model. On the basis of that, the dissertation analyzed an example study, with given parameters of the project, built and solved a static model and a dynamic model. The solving of the former one is to analyzing the impact of risks of decision making which was shown by different confidence level to Pareto solutions, the solving of the latter one is to analyzing the impact of preference of decision makers in different stages of the project to Pareto solutions.The results showed that, the fuzzy extent and dispersion degree of Pareto solutions reduced as the confidence level rise, indicating that the reduction of decision making risks made the solution set more clear, which is beneficial for decision makers to select the best solution; the distribution of Pareto solutions changed in different project stages, and had a better value in time, as the preference of the decision makers paid more and more attention on time rather than quality; the feasibility and reasonability of the model built by this dissertation has been verified. |
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