Scheduling Periodic Maintenance of Aircraft through simulation-based 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

3. 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 Failure repairs Unplanned Periodic maintenance Based on usage Different level maintenance facilities

4. Periodic maintenance of a Hawk Mk51 training aircraft

5. 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

6. Scheduling vs. no scheduling

7. 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

8. 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

9. 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

10. 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)

11. 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

12. Design of experiment • 300 evaluations of the simulation for each combination of parameters

13. Results • Highest average availability obtained in the optimization

14. 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