In order to cope with the turbulent environments in digital age, an enterprise should response to the changes quickly. Therefore, an enterprise must improve her ability of real-time decision-making. One way to increase the competence of real-time decision-making is to use Real-Time Decision Support Systems (RTDSS). A key feature for a Decision Support Systems (DSS) to successfully support real-time decision-making is to help decision-makers selecting the best models within deadline. This study focuses on developing methods to support the mechanism of model selection in DSS. There are five results in this study. Firstly, we have developed a time-based framework to evaluate models. This framework can help decision-makers to evaluate the quality and cost of model solutions. Secondly, based on the framework of models evaluation, we also developed three models selection strategies. These strategies can help decision-makers to select the best model within deadline. Thirdly, according the definitions of parameter value precision and model solution precision in this study, we conduct a simulation analysis to understand the impacts of the precision of parameter values to the precision of a model solution. Fourthly, in order to understand the interaction among the model selection variables, we also simulate the application of model selection strategies. The results of simulation indicate our study can support models selection well. Finally, we developed a structure-based model retrieval method to help decision-makers find alternative models from model base efficiently and effectively. In conclusion, the results of this research have drawn a basic skeleton for the development of models selection. This research also reveals much insight into the development of real-time decision support systems.