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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
