Abstract This research addresses the cockpit crew rostering problem in a low-cost airline. Through interviews with the aircrew planner, four objectives are considered vital for effective roster preparation; minimisation of nautical mile cost, balance workload among cockpit crews, maximisation of preferential requests from senior pilots and minimisation of the number of repeated flight patterns flown by individual pilots. Since the problem is NP-hard with many conflicting objectives that need to be optimised simultaneously, multi-objective evolutionary optimisation is an effective technique to solve this problem. As a result, the hybridisation between MOEA/D and HBMO algorithms, namely MOEA/D-HBMO, is developed. The proposed algorithm is compared to MOEA/D and HBMO. It is observed that MOEA/D-HBMO outperforms the counterparts in convergence related metrics and it could discover the best extreme points to meet every objective.

中文翻译：

---

多目标低成本航空公司座舱机组人员排班优化

摘要 本研究解决了低成本航空公司的驾驶舱机组排班问题。通过与机组规划人员的面谈，四个目标被认为对于有效的名册准备至关重要；最小化海里成本、平衡驾驶舱机组人员之间的工作量、最大化高级飞行员的优先请求和最小化单个飞行员重复飞行模式的次数。由于该问题是 NP-hard 问题，有许多相互冲突的目标需要同时优化，多目标进化优化是解决这个问题的有效技术。因此，开发了 MOEA/D 和 HBMO 算法之间的混合，即 MOEA/D-HBMO。将所提出的算法与 MOEA/D 和 HBMO 进行比较。