在當前的醫療環境中，護理師的快速流失與人手不足問題日益嚴重。一份良好的護士班表對流程效率和工作環境穩定性的貢獻尤其不可忽視；然而，經國際護理協會(International Council of Nurses, ICN) 暨亞洲護理人力論壇 (Asia Workforce Forum, AWFF) 之研究顯示：在2020-2022年期間的COVID -19疫情全球大流行之際，過量壓力導致大量護理師創傷、出現罷工狀況、工作分配不平均、護理人員教育中斷等，使人員短缺問題更加惡化，含有執照的護理人員執業率僅62.7％。因此呼籲各國政府應致力於投資護理教育、領導、就業和服務，以招募和留任更多護理人員，實現全民健康覆蓋，以維護護理人員之權益，提升整體醫療服務績效。而護理師的流失導致新進員工需要面對複雜的醫療情境，使得整個部門始終處於青黃不接的人員短缺狀態。

本研究為因應以上問題，重新評估和調整護理人員的排班策略，並引進人員決策風格變數 (Decision-Making-Styles, DMS)，同時考量國內護理人員適用之最新勞基法規定，以連續時間序列形式融合國內常使用之三班制規範，設置一映射函數設置使班次跟天數之間相互轉換，擬以一創新的數學規劃模型來求解護理人員排班問題，使排班模型更具彈性和人性化，並最大程度地減少人員之間的不適配程度，以期提升整體醫療服務的品質和效率。最後運用混合數學規劃最佳化模型求出可行班表，以及啟發式演算法中的基因演算法進行大規模實例求解本研究之一般化護理人員排班模型。

In the current medical environment, the rapid turnover and shortage of nursing staff is becoming increasingly serious. A good nurse scheduling is particularly important for the efficiency of processes and the stability of the work environment. However, research by the International Council of Nurses (ICN) and the Asia Workforce Forum (AWFF) has shown that during the global COVID-19 pandemic from 2020 to 2022, excessive stress led to a high incidence of nurse trauma, strikes, uneven work distribution, interruptions in nursing education, and arbitrary rotation among departments, exacerbating the shortage of personnel. The loss of nursing staff has forced new employees to face complex medical situations, keeping the department in a constant state of personnel shortage.
In response to these challenges, this study re-evaluates and adjusts the scheduling strategies of nursing staff, introducing the Decision-Making Style variable (DMS), and considering the latest labor law regulations applicable to domestic nursing staff. It adopts the commonly used three-shift system in the country, setting up a mapping function to convert shifts and days. The study proposes an innovative mathematical programming model to solve the nursing staff scheduling problem, making the scheduling model more flexible and humanized, with the aim of improving the quality and efficiency of healthcare services. Finally, the study uses a hybrid mathematical programming and genetic algorithm from heuristic algorithms to solve large-scale instances.

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