Choosing an efficient time representation is an important consideration when solving short-term scheduling problems. Improving the efficiency of scheduling operations may lead to increased yield, or reduced makespan, resulting in greater profits or customer satisfaction. When formulating these problems, one must choose a time representation over which to execute to scheduling operations. We propose in this study an iterative framework to refine an initial coarse discretization, by adding key time points that may be beneficial. This framework is compared against existing static discretizations using computational experiments on a scientific services facility. Using case studies from other applications in chemical engineering, we compare the performance of our framework against a previously reported time-discretization approach in the literature. The results of these experiments demonstrate that when problems are sufficiently large, our proposed dynamic method is able to achieve a better trade-off between objective value and CPU time than the currently used discretizations in the literature.