Phase-out maintenance optimization for an aircraft fleet

This paper presents a novel approach for cost-effective optimization of stop-maintenance strategies for a set of repairable items (rotables).

The optimization method has two steps. First, the novel concept of matrix simulations is introduced to locate the solution space of the optimization problem in question. Second, a genetic algorithm is applied to find the minimum cost solution. The combination of matrix simulations and genetic algorithm is shown to constitute a powerful method for solving the optimization problem in a fast manner. To demonstrate the efficacy of the proposed method, it is compared with a crude search, and a steepest descent algorithm. Our proposed method is faster than the crude search and also locates the optimum more often than the steepest descent search. The method is illustrated by applying it to a phase-out scenario of an aircraft fleet, where the optimal stop-maintenance strategy is determined for a set of rotables.


  • Shows how a phase out optimization problem can be handled with steepest descent.
  • Shows how a phase out optimization problem can be handled with a genetic algorithm.
  • Fast localization of the solution space using matrix simulations and binary search.
  • Shows that the genetic algorithm is able to find the optimum for the studied case.
  • Shows that genetic algorithm is a very fast approach to solve the given problem.

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By Olle Wijk, Patric Andersson, Thord Righard from Systecon and Jan Block from SAAB