Individual scheduling approach for multi-class airline cabin crew with manpower requirement heterogeneity

Xin Wen, Sai Ho Chung (Corresponding Author), Ping Ji, Jiuh-Biing Sheu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


The cabin crew pairing problem is one of the major challenges faced by airlines. Traditionally, multi-class cabin crews are scheduled on a team basis separated by aircraft types (families) as cockpit crews. However, the manpower requirements for cabin crew across aircraft types (families) are heterogeneous, which cannot be handled by the team scheduling approach. Thus, some airlines nowadays are adopting the individual scheduling approach to deal with the flight manpower requirement heterogeneity. Motivated by the emergence of the individual cabin crew pairing practice, we conduct an analytical study which aims at improving manpower utilization while reducing costs by utilizing a new individual cabin crew pairing generation approach. We also mathematically formulate crew substitution in the model which can help hedge against manpower requirement variations. The impacts of the relationship between manpower availability with requirement benchmarks on cabin crew scheduling strategies are investigated to derive deep insights. A column generation based solution approach is developed. Computational experiments based on small-scale real-world collected flight schedules are conducted to test the distinctive features of the proposed individual pairing model. In addition, a series of large hypothetical instances are employed to examine the advantages of the proposed model over the traditional team-based model in terms of manpower utilization improvement and cost reduction through a customized Genetic Algorithm.

Original languageEnglish
Article number102763
Number of pages23
JournalTransportation Research, Part E: Logistics and Transportation Review
Publication statusPublished - Jul 2022


  • Airline scheduling
  • Cabin crews
  • Column generation
  • Manpower requirement heterogeneity


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