良好的醫療物資輸配送作業規劃，可幫助醫療系統於緊急疫情爆發時，有效率地服務突然大增的病患物資需求，以確保醫療系統物資有效率地應用以維持一定的醫療服務品質。目前國內大型醫療院所之短期物資輸配送作業規劃上，通常由醫療系統成立之聯合決策中心以人工的經驗來制定，此作法不具系統性分析，且相當依賴相關作業人員的主觀判斷，因此常可能導致不佳的決策。尤其，在疫情爆發時，物資需求突然大量增加，可能導致原先規劃的物資配送結果難以運用於實際作業中，且現有的決策作法不易有效地調整現有的資源以因應擾動事件。緣此，本研究針對疫情發生時，在給定各時點的預期物資需求下，考量各醫療院所的存貨容量限制、物資運送量及其他相關限制，利用時空網路流動技巧，建構一醫療物資輸配送模式及一動態決策架構，以幫助業者有效地進行醫療物資輸配送規劃。最後為測試本研究模式可能的實用績效，本研究以國內一大型醫療系統的營運資料為例，進行範例測試與分析，測試結果發現，本研究模式及動態決策架構除能有效調配現有的資源外，其結果亦較實務規劃作法為佳，顯示本研究所建構之模式與動態決策架構，應可為醫療管理當局有效地規劃醫療物資輸配送，及進一步發展更完善模式之參考。

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.