The Airline Crew Pairing Problem (ACPP) which consists of finding crew itineraries and satisfying the related law and regulation constraints is a significantly economic challenge. And many efforts have been spent by airline industry in the search for efficient and effective solutions. Instead of using the traditional set partitioning model, a different view is adopted here to model the crewing problem and formulate it with a set of combinational optimization equations. In general, there are two phases in crew pairing, such as pairing generation and pairing optimization to be solved. A method of inequality-based multiobjective genetic algorithm (MMGA) is used here to provide the solution and solve them at the same time. Besides, with the Method of Inequalities (MOI), designers can configure the ranges of solutions by adjusting an auxiliary vector of performance indices. In practice, the proposed MMGA approach possesses the merits of global exploration and can provide several optimal or feasible solutions to help planners perform efficient and effective decision-making.飛航組員之配對問題包含了搜尋組員的排程路線及配合相關的法律及規範限制。此一問題牽涉到相當大的飛航經濟成本，許多航空公司一直花費許多的人力、財力，尋求經濟及有效的解決方案。有別於傳統所使用的集合-分割方式，本論文採用不同的觀點建立配對模型並將此問題轉換成組合最佳化之問題。
一般而言，此配對問題牽涉到產生配對組合及配對最佳化兩個階段。論文中，將利用基於不等式之多目標遺傳演算法(MMGA)同步討論並求解出此兩階段問題。此外，藉由不等式之操作，設計者可以設定效率指標輔助向量以調整相關解集合的範圍。在實務上，此一方法具備了全域的搜尋能力並能產出多個最佳或可行解，對於規劃者，將可作為一實際且有效的決策工具。
