| 摘要: | 良好的醫療物資訂購與內部配送排程規劃，可幫助醫療業者有效地降低營運成本，並提升醫療服務品質。目前國內醫療院所對於醫療物資訂購/配送頻率、訂購量及安全存量等重要參數值的決策，仍依靠人工的經驗來制定，此作法不具系統性分析。此外，在實際的營運中，醫療物資需求常會隨著許多不確實因素產生擾動變化，可能導致原先規劃的結果失去其最佳性，造成營運成本大幅增加。緣此，本研究以醫院之立場，系統性考量醫院部門各時點的物資需求、存貨容量限制及其他相關限制，探討物資在時間面與空間面上的存貨與配送整合作業，構建確定性與隨機性醫療物資訂購與輸配送排程模式，以幫助醫療院所在實際營運中有效地規劃醫療物資訂購及配送作業，增進醫療資源使用效率。 本研究利用時空網路流動技巧，以系統最佳化的觀點構建模式。在求解上，利用問題分解策略，並結合CPLEX數學規劃軟體，發展啟發解法，以有效地求解模式。為評估確定性與隨機性模式所求得之排程結果，在隨機營運環境中的績效優劣，本研究發展一評估方法。最後，為測試模式與求解演算法於實務營運之績效，本研究以國內某家大型醫療院所的營運資料為例，進行實例測試與分析。 A satisfactory schedule of medical resource supply orders and transit plan can help medical institutions efficiently reduce the operating cost and to promote the medical service quality. In currently Taiwanese medical institutions, 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. Additionally, in actual operations, the demands of medical goods often change stochastically, possibly causing the original schedule to lose its optimality. It is difficult to efficiently revise the original schedule with existent resources to respond the changes. Consequently, the effect of medical system will be decreased and the operating cost be increased. Therefore, in this research, based on a medical institution’s perspective, we systematically consider the demand of goods for every time slot in all hospital departments, 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 and a stochastic medical goods order and transit scheduling model. These two models are expected to be useful planning tools for medical institutions to determine effective resource supply orders and transit schedules. We used time-space network techniques with the system optimization perspective to construct models. A simulation-based heuristic, coupled with Mathematical programming software, was further developed to efficiently solve the model. In addition, to evaluate the deterministic and stochastic scheduling models in actual operations, we developed a simulation-based evaluation method. Finally, in order to test the proposed models and solution algorithms in actual operations, we conducted a case study based on a domestic medical institution’s operating data. |
