  Due to complex sets of interrelated activities in aircraft heavy maintenance
(AHM), many airlines have to deal with substantial aircraft maintenance
downtime. The scheduling problem in AHM is regarded as an NP-hard problem.
Using exact algorithms can be time-consuming or even infeasible. This article
proposes genetic algorithms for solving the resource-constrained project
scheduling problem (RCPSP) in AHM. The objective of the study was to minimise
the makespan of the maintenance plan. The proposed algorithms applied five
heuristic dispatching rules to generate an initial population based on activity
list formation. Resource allocation methods for RCPSPearliest start time (EST)
and workgroup and earliest start time (WEST)-were used to evaluate the fitness
value. The elitist and roulette wheel methods were applied in the selection
process. The sequences of the activity lists were then iteratively improved by
crossover and mutation operations. The results show that the proposed
algorithms perform efficiently compared to the existing solutions in terms of
computational time and resource allocation.
