Scheduling Periodic Maintenance of Aircraft through simulationbased optimization

1
Scheduling Periodic Maintenance of Aircraft
through simulation-based optimization

• Ville Mattila and Kai Virtanen
• Systems Analysis Laboratory, Helsinki University
  of Technology

2
Contents

• The need for periodic maintenance (PM) scheduling
• Scheduling of PM tasks in the Finnish Air Force
  (FiAF)
• A simulation-based optimization model for the
  scheduling task
• Results from an example scheduling case

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Aircraft usage and maintenance
Usage
Maintenance
Pilot and tactical training, air surveillance A
number of aircraft chosen each day to flight
duty Several missions during one day
Periodic maintenance Based on usage
Failure repairs Unplanned
Different level maintenance facilities
4
Periodic maintenance of a Hawk Mk51 training
aircraft
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The need for PM scheduling

• Scheduling is done for two primary reasons
• Avoid degradation of aircraft availability
• Allow maintenance facilities to plan for supply
  of resources

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Scheduling vs. no scheduling
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Difficulty of scheduling

• Starting times of PM tasks can not be assigned
  with certainty
• Timing depends on the maintenance interval and on
  the usage of the aircraft
• Usage is affected by unexpected failures and
  subsequent repairs
• Intervals are not adjusted during normal
  conditions

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Maintenance schedule

• A maintenance schedule consists of targeted
  starting times of PM tasks
• The schedule is used to allocate flight time
  among aircraft by prioritizing aircraft with the
  highest ratio of
• The allocation governs the accumulation of flight
  hours and the actual timing of PM tasks

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The maintenance scheduling problem

• N the total number of aircraft
• X(x1,1,...,x1,n1,...,xN,1,...,xN,nN) the
  maintenance schedule of the fleet
• L simulated average aircraft availability
• ? sample path

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The simulation optimization model

• A discrete-event simulation model
• Describes aircraft usage and maintenance under a
  given maintenance schedule
• Returns aircraft availability as output
• A search method
• Produces new schedules based on the simulated
  availabilities
• A genetic algorithm (GA) or simulated annealing
  (SA)

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A case example

• The scheduling case
• A fleet of 16 aircraft
• A time period of 1 year
• 4 of the aircraft each perform 4 daily flight
  missions
• 4 PM tasks scheduled per each aircraft in the
  fleet
• The performance of different configurations of GA
  and SA in the case are compared

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Design of experiment

• 300 evaluations of the simulation for each
  combination of parameters

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Results

• Highest average availability obtained in the
  optimization

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Analysis of the obtained schedule

• The simulation can be used to further assess the
  schedule obtained in the optimization
• The queuing times in the maintenance facilities
  indicate whether the schedule can still be
  improved
• The simulation also provides information on the
  distribution of times, when the PM tasks are
  actually materialized

15
Concluding remarks

• The presented model has been implemented as a
  design tool for FiAF
• Final validation can be conducted by comparing
  actual flight operations and maintenance with the
  simulation
• Future work includes the consideration of task
  priorities in the optimization problem