Aircraft Fleet Maintenance Scheduling: Heuristics for Workload Balancing

Mikael Öhman, Mikko Laine, Jan Holmström

Research output: Contribution to conferencePaperScientific


The intensifying competition in the aviation industry forces airlines to constantly improve fleet utilization and reduce operating costs. For airline maintenance operations this translates to shorter maintenance windows and reduced maintenance resources. Considering the maintenance workload, these trends manifest themselves as reduced demand- and supply flexibility. In order to cope with the situation, airlines need to develop new approaches to maintenance management. In this paper we explore the case of a Nordic airline that has achieved one of the highest fleet-utilizations in the industry, while significantly reducing maintenance resources. We study how maintenance scheduling has adapted to the reduction in demand- and supply-flexibility, and evaluate two different approaches developed to accommodate the increased volatility in the operating environment; balancing workload through improved forecasting, and balancing workload though introducing fleet level planning-heuristics. Our results show that although increasing information intensity in planning leads to an optimal solution with respect to total workload, its benefits relative to the planning-heuristics approach are small and questionable. The contribution of the case research is to challenge the notion that the optimal solutions of information-based approaches justify their adaptation. The alternative heuristics approach gives a near-optimal solution while requiring much less effort to implement and maintain.
Original languageEnglish
Publication statusPublished - 2016
MoE publication typeNot Eligible
EventInternational Working Seminar on Production Economics - Innsbruck, Austria
Duration: 22 Feb 201626 Feb 2016
Conference number: 19


SeminarInternational Working Seminar on Production Economics
Abbreviated titleIWSPE


  • Aircraft maintenance
  • Maintenance planning and scheduling
  • Workload balancing
  • Case study


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