Abstract: To overcome the application limitations of stochastic programming, fuzzy programming and uncertain programming expected value model, a uncertain chanceconstrained programming model is proposed based on optimistic and pessimistic values. Firstly, the optimistic and pessimistic values of the revenue function and cost function are defined based on the classification of objective functions. On this basis, the Maximax, Maximin, Minimin, and Minimax models of uncertain chanceconstrained 〖JP2〗programming are established. Secondly, the equivalent transformation methods are proposed， making it possible to solve the uncertain chanceconstrained programming models directly. The influences of belief degree on the models are also studied. Finally, the proposed models, methods and the influences of belief degree are analyzed by an example of the travel time planning problem of an express delivery network in Xi’an. And it is also pointed out that the belief degree can reflect the risk preference of the decision makers in this case. The results prove the feasibility, validity and practicability of the proposed models and methods.