| Uncertain programming is a favorable tool for handling all kinds of optimization problems under uncertain environment, and it is more practical than we usually used deterministic optimization model. It was divided into three types that the expected value model, chance-constrained programming model and dependent-chance programming model. On the basis of uncertainty theory and existing uncertain programming model, because of shortcoming of objective function takeing expected value of the existing uncertain programming model(when is poor stability of uncertain variables), to this kind of model is revised and improved.The main work is as follows:Firstly, based on uncertain theory, a class of uncertain linear chance-constrained programming model that objective function to obtain the most value under a certain confidence level, and gives the solving algorithm of this model was studied. Then, the model is applied to the transport capacity constraints of the transportation problem, by mean of given a numerical example illustrate the rationality of the model. Secondly, a class of uncertain multi-objective chance-constrained programming model was discussed, and a hybrid intelligent algorithm was designed by integrating genetic algorithm and inverse uncertainty distribution method for solving this model. The uncertain multiobjective chance-constrained programming model of assignment problem with uncertain factors was studied and to a hybrid intelligent algorithm for solving was designed. Finally, a numerical example illustrate the rationality of the model was given. The third, a class of uncertain goal chance-constrained programming model is established, which minimizing uncertain objective function with a given deviation between the ideal value and, a algorithm is presented of solving the model. Having applied this model to the uncertain environment of assignment problem, practical examples prove the validity of the rationality of the model and the algorithm. |
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