Hybrid multi-objective metaheuristic algorithms for solving airline crew rostering problem with qualification and...

Area of Science:

• Operations Research
• Artificial Intelligence
• Aviation Management

Background:

• Rapid growth in flights and limited pilot availability necessitate efficient crew allocation strategies.
• Inadequate crew rostering can lead to flight cancellations due to pilot shortages.

Purpose of the Study:

• To develop a robust solution for the airline crew rostering problem (CRP).
• To optimize pilot scheduling considering qualifications, language proficiency, fairness, and satisfaction.

Main Methods:

• Formulated the CRP as an integer programming model with multiple constraints and objectives.
• Designed two hybrid metaheuristic algorithms: a genetic algorithm combined with variable neighborhood search and the Aquila optimizer.
• Evaluated the algorithms' performance through simulation.

Main Results:

• The proposed approach successfully preserves system fairness.
• Maximizing fairness was achieved, but this came at the cost of reduced crew satisfaction.
• The algorithms effectively handle the trade-off between fairness and crew satisfaction.

Conclusions:

• The developed model and algorithms provide an effective method for airline crew rostering.
• Prioritizing system fairness in crew scheduling is feasible, though it impacts pilot satisfaction.
• This research offers a valuable tool for airlines facing crew allocation challenges.