因新冠肺炎(COVID-19)於各國間流行，使航空產業受到很大的影響，對於入行門檻較低的航空站地勤業而言，因減班休息的緣故，許多員工決定轉換跑道而離職。如今，隨航班逐漸的恢復，對於人力的需求日益漸增，必須重新招募人員，然而對於何時要招募、需要招募多少人等問題，本研究對象過往均仰賴幹部或有經驗之人員作判斷，並沒有一套標準的判斷方式及數據可參考。
然而對於勤務公司而言，人力的運用相當重要，若人力過剩會產生閒置人力，若人力不足則會使作業有困難或者有員工過勞之情況發生，且此情形隨著各航班的恢復，及員工招募不易的問題而日益嚴重。因此，本研究透過系統化的方式改善現況，並建立相關標準及數據供排班人員及管理者參考，同時透過優化班表，以最精簡的人力做最佳的安排。
本研究以台北松山機場某航勤公司之機坪作業組做為研究對象，運用三階段的方式分析。首先在組別需求問題，將航班時刻表轉換為組別需求，並透過建立的混和整數線性規劃模型，求解最佳組別組合。由於實際航班可能有延誤或早到情況，所排定之人力班表最好能因應航班之到離衍生之需求，故本研究參考過往航班到離，應用模糊理論求出一模糊班表，並求出一模糊組別需求。在人力配置方面，本研究各組有4人及5人配置人力差異，且考量管理階層尖峰時段有限度支援。接著探討正常航班組別需求及模糊組別需求等之人力配置在不同情境中各項指標的表現，經過比較後選出最佳的組別組合及人力配置。最後，在組員指派問題中，也考量勞基法規定及休假需求，建立另一個混和整數線性規劃模型，求解具有公平性之月班表。
研究結果顯示：模糊班表產生之組別及人力配置因應能力大幅提升，使用管理階層支援時段減少約36%，增加費用約11%及增加人數約12%。本研究並提供各項視覺化圖表，使排班人員及管理者更快速的檢視成果。此外，在求解所花費的時間上，與傳統人工思考相比，亦能大幅減少排班人員的作業時間。

The COVID-19 pandemic has significantly impacted the aviation industry, particularly affecting airport ground handling services with lower entry barriers. Many employees in this field have decided to change their jobs due to flight reductions. As flights gradually recover, the demand for manpower is increasing, necessitating the rehiring of personnel. However, decisions on when to recruit and how many employees to hire have traditionally relied on experienced personnel or management, lacking a standardized approach and reference data.
This study employs a three-stage analysis approach. Firstly, addressing group requirements, flight schedules are transformed into group demand. A mixed-integer linear programming model determines the optimal group combinations. Recognizing flight might have delay or early arrival, this study applies fuzzy theory to generate a fuzzy flight schedules and also determines the optimal group combinations. Secondly, staffing involves the assignment of 4 or 5 members for each individual group, considering limited management support during peak periods. The study compares the performance of normal and fuzzy group staffing requirements under various scenarios. Optimal group combinations and staffing setups are selected. Third, another model is formulated for a fair monthly, take into account of labor laws and leave requirements.
Results show that fuzzy schedules and staffing requirements significantly enhance flexibility, reducing management support during peak hours by about 36%, increasing costs by approximately 11%, and boosting personnel by around 12%. The study provides quick visual displays for managements to reduce the planning and decision time for personnel scheduling and rostering compared to traditional manual processes.

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