Institute of Transportation, MOTC

　　A good medical goods transit scheduling plan helps the medical system solve unexpected situations efficiently when an urgent epidemic breaks out. Currently medical goods are usually scheduled by the united decision center of Taiwan medical systems. However, the schedules are manually determined by staff with experience, without optimization from a systemic perspective. In particular, in an urgent epidemic situation, the demand would increase drastically and thereby the original plan could not be effectively applied. Therefore, in this research, based on a medical system’s perspective, given the estimated demand for every time slot in all hospitals and their departments, we consider the stock capacity for all hospitals and their departments as well as transshipment and other related constraints, and then employ the time-space network technique to construct a medical goods transit scheduling model, coupled with a dynamic decision framework, to help the medical systems efficiently schedule their medical goods transshipments. Finally, to provide a preliminary evaluation of the model in practice; we perform a case study based on a domestic large-scale medical system’s operating data. The test results indicate that the proposed model and the dynamic decision framework can not only allocate the resources efficiently, but can also yield better solutions than the manual method in practice, showing that the model and the dynamic decision framework could be useful reference materials for medical systems to effectively plan their medical goods transit schedules and further to develop more robust models.