航空產業疫情期間經歷長期低迷，許多機場被迫縮減營運規模，而隨著全球疫 情和緩與航空產業復甦，各國面臨旅客流量快速攀升趨勢，其中協助行動不便旅客 於機場順利完成搭機流程，機場護送員扮演重要角色。實務上，現今傳統為人工調 度方式，缺乏整體規劃，容易導致資源浪費或服務品質下降，造成旅客等待時間過 長，以及機場服務品質下降等問題。因此，如何靈活且有效調度護送員，為機場決 策者需要面臨與解決之問題。 本研究結合時空網路與數學規劃方法，於滿足實務限制條件下，以最小化總人 力成本為目標，提出彈性排班策略，構建機場護送員人力規劃模式，同時發展流量 分解方法以公平指派任務。鑑於求解問題規模龐大，難以於合理時間內利用數學規 劃軟體求出最佳解，故本研究採用 C++程式語言及 CPLEX 數學求解軟體，配合鬆 弛固定演算法概念設計三階段啟發式方法以有效求解。最後，針對不同參數條件下 進行敏感度分析與方案分析，結果顯示本研究發展之模式與演算法皆具成效良好。 期望本研究能解決實務人力規劃問題，協助機場決策者做出抉擇，並將研究結果供學術界參考。;The aviation industry experienced a prolonged downturn during the COVID-19 pandemic, forcing many airports to scale down their operations. As the global pandemic subsides and the aviation sector gradually recovers, countries are now facing a surge in passenger traffic. Among the various operational needs, escort staff play a crucial role in assisting passengers with reduced mobility(PRMs) to complete the airport boarding process smoothly. In practice, current escort staff scheduling primarily relies on manual dispatching, lacking systematic planning. This often leads to inefficient resource allocation, increased passenger waiting times, and a decline in overall service quality. To address these challenges, this study integrates time-space network modeling with mathematical programming techniques to develop a flexible escort staff scheduling strategy under realistic operational constraints, aiming to minimize total labor costs. A flow decomposition approach is proposed to ensure fair task assignment among staff. Given the large-scale nature of the problem, obtaining optimal solutions through exact mathematical programming within a reasonable time frame is difficult. Therefore, a three- stage heuristic method based on the Relax-and-Fix algorithm is designed and implemented using C++ and solved with CPLEX. Sensitivity and scenario analyses are conducted under various parameter settings, and the results demonstrate the effectiveness of both the proposed model and solution algorithm. This study aims to support airport decision-makers in resolving practical manpower planning challenges and to contribute valuable insights for future academic research.
