本論文探討航空業中空服員排班的重要性，著重於最佳化班表以滿足經濟成本、法規、工會工作限制、員工偏好等多方面需求。空服員排班問題在航空公司營運中具有複雜性，尤其在規模龐大的航空公司中更為困難。分析空服員排班的多個面向，包括性別、國籍、年資、個人喜好、專業能力等因素，並強調這些因素對空服員個人需求和工作效率的影響。考量航空公司目前使用的線上排班原則，但也指出了其局限性，尤其是在性別平衡、個人需求和喜好方面的考量不足。本研究分析台灣空服員性別比例的現況，與過去十年間國籍航空客艙空服員男女比的變化。進一步討論在不同性別比例下可能出現的管理、工作效率、心理壓力等問題，強調了性別平衡對於航空公司排班的重要性。
本論文深入探討航空業中空服員排班的複雜性，提出性別平等在這一過程中的重要性。由於航空產業的勞動密集性，人事成本相對較高，因此對排班工具的需求迫切。最佳化的空服員班表可以提供多方面的好處，但實現成本效益的排班同時滿足多樣需求，包括工作場所協議、輪班公平、員工偏好和兼職工作，需要實施精心設計的決策支援系統。航空公司的服務品質與員工對班表的滿意度密切相關，滿足員工期望也成為激勵員工的手段。

Airline crew scheduling has long been recognized as one of the most complex and computationally demanding optimization problems in transportation research and airline operations management. It involves not only the allocation of crew members to flights but also the consideration of multiple regulatory, operational, and human factors. Traditional approaches often emphasize efficiency, cost reduction, and compliance with labor laws. However, the role of gender distribution, an important yet often overlooked aspect in crew planning, has rarely been examined in depth. This study addresses this gap by developing and analyzing an optimization model for flight attendant scheduling that explicitly incorporates gender considerations. The objective is to ensure that each scheduled flight includes at least one male crew member while simultaneously maintaining fairness, regulatory compliance, and cost-effectiveness.
The research is based on a case study of Airline A, a major Taiwanese airline, which faces challenges arising from a significant gender imbalance among its flight attendants. While female flight attendants represent the overwhelming majority of the workforce, male attendants remain a small minority. This imbalance has implications not only for workload distribution but also for workplace culture, employee morale, and the quality of passenger service, particularly on long-haul and high-demand routes. In addition, feedback from airline management and industry stakeholders highlights the importance of addressing gender-related scheduling issues, both from an operational perspective and in terms of corporate social responsibility.
To address these challenges, this study formulates a mathematical programming model using the Gurobi Optimization solver. The model integrates key operational constraints, including maximum working hours, minimum rest periods, and rolling seven-day rest requirements, along with specific gender constraints to guarantee male crew participation across all flights. The design of decision variables, such as the binary assignment variable x[f,d,c] for crew-to-flight allocation and the indicator variable y[f,d] for male crew presence, reflects the need to balance computational tractability with the accuracy of operational representation. Several performance parameters—such as MIPGap tolerance, solution threads, and computation time limits—were carefully adjusted to ensure both efficiency and solution robustness.
The empirical analysis is conducted using actual flight data from Airline A, covering multiple days of operation and a diverse set of routes. Comparative experiments are designed to evaluate the performance of the proposed model under different conditions. Results are benchmarked against the airline’s current manual scheduling practices. The findings show that the optimized model consistently outperforms existing methods in achieving balanced gender allocation without significantly increasing staffing costs. Specifically, the model ensures that all flights have male attendants assigned while respecting rest regulations and minimizing idle crew time. Moreover, sensitivity analysis demonstrates that the model remains robust across variations in demand, crew availability, and gender ratios. Even under scenarios of extreme gender imbalance, the solver successfully finds feasible schedules within acceptable computational time.
Beyond operational feasibility, the study highlights broader managerial and social implications. From a workforce management perspective, gender-sensitive scheduling contributes to reducing employee dissatisfaction and mitigating absenteeism, particularly among male attendants who often face disproportionate workloads. From a service quality perspective, passenger satisfaction is likely to increase when crew composition is more balanced, as male attendants often play unique roles in handling physically demanding or safety-related tasks. Strategically, the findings provide airlines with insights into long-term recruitment planning, suggesting that a more deliberate approach to hiring male flight attendants can alleviate scheduling bottlenecks in the future.
This study makes several contributions to both theory and practice. Academically, it extends the literature on crew scheduling by incorporating gender as a decision variable—an element largely absent from existing research. Methodologically, it demonstrates the applicability of advanced mathematical programming and commercial solvers such as Gurobi to real-world workforce optimization problems in the airline industry. Practically, it offers airline managers a systematic framework for designing equitable and efficient schedules that reconcile operational requirements with human resource considerations. The study concludes by recommending further research on integrating additional social dimensions, such as seniority, cultural background, and employee preferences, into crew scheduling models. Such extensions could provide a more holistic perspective on how optimization techniques can support fairness and diversity in airline operations.
In summary, this thesis develops and validates an optimization-based framework for flight attendant scheduling with explicit gender considerations. The results confirm that gender-balanced scheduling is both feasible and beneficial, enhancing fairness, operational efficiency, and service quality without imposing prohibitive costs. These findings contribute to advancing academic discourse on workforce optimization and provide actionable strategies for the airline industry in promoting both efficiency and inclusivity.

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