| Summary: | 碩士 === 國立中央大學 === 土木工程研究所 === 95 === When an urgent epidemic situation breaks out, a satisfactory schedule of medical resource supply orders and transit plan can help a medical system efficiently serve drastically increased demand of medical goods to patients. It can also help effectively reduce the operating cost and maintenance the medical service quality. Currently medical goods are ordered by the union decision center in a Taiwan medical system with electronic purchase systems and decision support systems. However, the important parameters (e.g., the order/transit frequency, the order quantity, and the safe stock capacity) are manually determined by staff with experience. Lacking a systematic optimization analysis, this approach rather depends on the staff’s subjective judgments. As a result, feasible but inferior decisions have usually been made. In particular, under an urgent epidemic situation, the demand of medical goods would suddenly and largely increase, which would make it difficult to efficiently revise the original schedule with existent resources to respond to the incident. Additionally, in actual operations, the demands of medical goods often change stochastically, possibly causing the original schedule to lose its optimality. Consequently, the effect of medical system would be decreased and the operating cost be increased. Therefore, in this research, based on a medical system’s perspective, we systematically consider the expected epidemic period, the expected demand of goods for every time slot in all hospitals and their departments, the transportation cost of goods, the stock capacity and other constraints, as well as the integrated transit plan of medical goods in the dimensions of time and space, to construct a deterministic medical goods order and transit scheduling model. Further, considering the stochastic demand of medical goods that occur in real time operations, we construct a stochastic medical goods order and transit scheduling model. These two models are expected to be useful planning tools for medical system to determine effective resource supply orders and transit schedules under urgent epidemic situations.
We employed time-space network techniques with the system optimization perspective to construct a deterministic real-time scheduling model, which include several time-space networks to express the flows of different medical goods in the dimensions of time and space. The model is formulated as a multiple commodity network flow problem that is characterized as NP-hard. Then, we modified the fixed demand parameters in the deterministic scheduling model as random variables to develop a stochastic real-time scheduling model, which is more complicated than the deterministic scheduling model. To better solve the stochastic model, we used problem decomposition techniques to develop the heuristic to solve the stochastic problem. In addition, to evaluate the deterministic and stochastic scheduling models in actual operations, we develop an evaluation method. Finally, in order to test the models and solution algorithms in actual operations, we perform a case study based on a domestic large-scale medical system (containing several hospitals)’s operating data. The preliminary results are good, showing that the models could be useful for medical system planning medical goods order and transit scheduling.
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