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Due to complex sets of interrelated activities in aircraft heavy maintenance(AHM), many airlines have to deal with substantial aircraft maintenancedowntime. The scheduling problem in AHM is regarded as an NP-hard problem.Using exact algorithms can be time-consuming or even infeasible. This articleproposes genetic algorithms for solving the resource-constrained projectscheduling problem (RCPSP) in AHM. The objective of the study was to minimisethe makespan of the maintenance plan. The proposed algorithms applied fiveheuristic dispatching rules to generate an initial population based on activitylist formation. Resource allocation methods for RCPSPearliest start time (EST)and workgroup and earliest start time (WEST)-were used to evaluate the fitnessvalue. The elitist and roulette wheel methods were applied in the selectionprocess. The sequences of the activity lists were then iteratively improved bycrossover and mutation operations. The results show that the proposedalgorithms perform efficiently compared to the existing solutions in terms ofcomputational time and resource allocation.

Genetic algorithms for the resource-constrained project scheduling problem in aircraft heavy maintenance