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140 Nima Safaie

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140 Nima Safaie

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140 Nima Safaie

  1. 1. AIRCRAFT MAINTENANCE ROUTING Nima Safaei (PhD, M.Eng) Senior Specialist, Operations Research Maintenance Engineering Bombardier Aerospace
  2. 2. Big Picture 2
  3. 3. Airline Operations 3 MTC: Maintenance Aircraft MTC Routing Market DemandMarket Demand MTC Tasks Consolidation MTC ConstraintsMTC Constraints MTC CapabilitiesMTC Capabilities MTC ResourcesMTC Resources Tail RotationTail Rotation Fleet AssignmentFleet Assignment Flight ScheduleFlight Schedule Traffic ForecastingTraffic Forecasting MRM/MPDMRM/MPD LabourLabour SlotSlot FacilityFacility SkillSkill TCCA/FAATCCA/FAA MTC Tasks Scheduling Disruption  Recovery (re‐planning) DiversionDiversion CancellationCancellation MTC Resource Planning MTC  Opportunities MTC  Opportunities Forecasting  through   Simulation MTC Facility Planning Work PackagesWork Packages DelayDelay Fleet Utilization Optimization Flight Schedule Improvement Data Storage Outputs Crew RegulatoryCrew Regulatory
  4. 4. • Service & Routine Checks: General visual inspection Every 36, 50, 65, and 100 FH • Line Check (Type A): Detailed visual inspection, lubrication Every 600, 1200, 1800, 2400, 3000, and 3600 FH • Base Check or Heavy/Hanger Maintenance (Type C): Detailed inspection, Restoration, and discard  Every 6000, 12000, 18000, 24000, 30000 FH • Calendar Check Every 1, 6, 12, 24, 36, 60, 72, 108, 120 and 180 Months • Out of Phase Items: inspections, certification or warranty requirements which do not fall on the same date as the annual inspections • Out of Service Items (Unscheduled MX): MX work that has not been included on the approved MX Schedule prior to its commencement. Ranged between 200 to 40000 FH • Fatigue Tasks: fatigue damage detection and correction Ranged between 250 to 100,000 FC • ½ Life Check: A conservative overall inspection program based on economic life of aircraft. 4 Aircraft Approved Maintenance Program
  5. 5. Current Practice 5
  6. 6. Network  Planning Fleet  Assignment Staffing Facility  Planning Overview 6 Tail  Assignment Crew Planning Fleet  Maintenance Interruption  Recovery Aircraft  Routing Unexpected  Events Uncertainty Non‐optimized  Interaction Strategic Level Operational Level Optimized Interaction Inaccurate Labour‐hour Estimation • Trivial algorithms • Poor procedures Millions of possible  scenarios are overlooked  Millions of possible  scenarios are overlooked  Low‐quality solutionsLow‐quality solutions Increased costs & riskIncreased costs & risk ConstraintsDemand Low predictabilityLow predictability
  7. 7. BOMBARDIER PROPRIETARY INFORMATION7 Long‐term Planning vs. Short‐term Operations Event Weather Mechanical Medical Operational Recovery flight Ferry flight Swapping Tail re-routing MX re-scheduling Crew re-paring Lack of spare Cascading delays lengthy turn times NSP: Network Schedule Planning OCC: Operations Control Center MCC: Maintenance Control Center MCP: Maintenance Control Planning Causes Solutions NSP: Six-month forecasting on Line-of-Flights (LOFs), tail assignment and crew planning based on master flight schedule and crew regulatory requirements OCC: Six-week tail assignment and crew planning (Saber system) OCC: 3-day crew scheduling MCC: Bi-weekly maintenance packaging Visibility: TRAX Strategy: Manually using due dates calculated based on average utilization rate per day OCC: 3-day MX scheduling Visibility: Saber for MX Opportunity Technicians availability (Excel sheet) Approach: Manually Strategy: experimental A/C damage
  8. 8. BOMBARDIER PROPRIETARY INFORMATION8 Maintenance Event Scheduling Maintenance  Scheduler Control  Center Aircraft  Operations Coordination between Groups Flight Schedule  Planner • Each group has own KPIs, priorities and processes • Insufficient coordination and harmonization between the groups • Up to 80% of the tasks are manually performed based on individual knowledge. • The software packages are mostly used for visibility and feasibility check not optimization. • Significant gap between the initial long‐term plan and short‐term schedules. • Dispatch Reliability concern: Increased fleet utilization negatively affects the non‐chargeable interruptions? Crew Scheduler
  9. 9. BOMBARDIER PROPRIETARY INFORMATION9 • Creating Eulerian routes so that each aircraft must visit a maintenance base every n days. Cyclic rotation is not an optimal strategy because o It is a conservative strategy: The penalty for not doing maintenance is significantly higher than the cost of doing it, so most airlines are very conservative and leave a good margin of error. o Individual aircraft vary in age and utilization rate. So, each tail number has a different maintenance workload and pattern from one period to another. o Maintenance tasks are defined in terms of various flight attributes, e.g., Flight Hour/Cycles. Engine Hour/Cycles, Time Calendar, and APU Hour. o Maintenance tasks vary in duration, labor and skill requirements. o All maintenance stations do not have the same facilities, tools, skills, and capabilities. • Shortfalls: o Uneven fleet utilization o Fluctuated maintenance workload o A high level of premature maintenance o A low level of maintenance task yield (percentage of the task interval satisfied)   o A high cost/time of interruption recovery in the presence of unexpected events.  • Literature Gap: o The related literature does not consider the maintenance requirements of individual airplanes o The related literature only considers the most frequent maintenance types not the full range. Cyclic Rotation
  10. 10. Integrated Operations 10
  11. 11. BOMBARDIER PROPRIETARY INFORMATION11 InAirM aS Line-of- Flights (LOFs) Generation Tail Rotation Maintenance Consolidation & Scheduling • InAirMaS ensures that the right aircraft is in the right place at the right time to undergo maintenance. • With InAirMaS, your fleet will spend more time in the air, earning revenue, reduce the maintenance costs, increase the dispatch reliability, and mitigate the flight interruptions impact. • Efficient and automatic interaction embedded in InAirMaS enable us to optimally coordinate various functions at the same time. • one single database–one single point of entry for maintenance and fleet operations departments • Powered by advanced optimization algorithms, predictive analytics, and artificial intelligence. Integrated Aircraft Routing and Maintenance Scheduling  (InAiRMaS) What maintenance tasks should be done on which aircraft at which maintenance station and when? The sequence of flights operated by a single aircraft satisfying crew regulatory requirements The sequence of LOFs operated by a particular aircraft.
  12. 12. BOMBARDIER PROPRIETARY INFORMATION12 • Runs millions of tail rotation, LOF*, and maintenance task scheduling scenarios in a matter of minutes to provide high quality schedules with least cost* and risk*. • Increases the predictability and decreases the reaction time for interruption recovery (real‐time scheduling and swift re‐planning) • Employs a simulation‐optimization module to: o Perform long‐term planning: o How much labor‐hour is required and where? (staffing at stations) o Where are the best locations for maintenance facilities and spare fleet? o What if a maintenance facility is relocated from station A to B? o Helps your airline identify opportunities to accommodate a high fleet utilization at a reasonable cost and time: o Increased maintenance check yield  Decreased check frequency  Increased fleet uptime  increased potential revenue o Flight schedule improvement: Introducing potential flight legs which can be inserted into the current schedule and flown by current fleet. o Extracts strategic scenarios for growth through what‐if scenarios analysis: o Join the Dots: Flights between existing destinations not currently flown by airline o New Destinations: Flights to/from new destinations not currently served by airline o What is the impact of improving aircraft turns‐time by, say, 5 or 10 minutes? InAirMaS: LOF: Line‐of‐Flight satisfying crew regulatory requirements Least Cost: Minimized number of non‐revenue flights and maximized maintenance checks yield Least Risk: a solution with high flexibility and least sensitivity to unexpected interruptions
  13. 13. BOMBARDIER PROPRIETARY INFORMATION13 InAirMaS: 100% Efficiency = Zero non‐revenue flight + Zero flight delay + 100% maintenance task yield (never happened) Now Efficiency  100% Learning Phase Simulation Phase Improvement  Time Learning from historical data to tune up the  simulation parameters Accurate forecasting of future Optimization  (InAirMaS) o Opportunity to optimize operations based on forward forecasting predicted on known historical events. o 'What if' scenario planning provides impact analysis with the ability to review plans before release to publication. o Automated data integration with a high visibility for management (aircraft routing, scheduled checks, unscheduled maintenance, delays, cancellations, diversions, non‐revenue flights, …) o Capability for learning from historical observations to forecast the future for strategic‐level decisions • Simulate the future to forecast it precisely by providing
  14. 14. BOMBARDIER PROPRIETARY INFORMATION14 Simulation Process for Long‐term Forecasting  OOOI history Flight Interruptions history Non‐rev. flights history MX checks history MX Facilities and Capabilities per location Skilled‐labour availability per location Cleaning Analysis Learning Integration Simulation Comparison MX: Maintenance OOOI: (OUT gate)‐ (OFF wheel) – (ON wheel)‐ (IN Gate) times Flight Schedule Fleet Information Approved MX Program Data Mining Verification Output Options Inputs  (Specific time horizon) Optimal  Fleet Size Spare fleet  planning MX facility  planning MX workforce  planning Flight schedule  improvements What‐if scenarios on establishing new  bases or relocation of existing ones Where and when? Where and how  many? InAirM aS LOF Generation Tail Rotation MX Checks Consolidation MX Checks Scheduling Optimized Fleet  Utilization Maximized fleet  availability 
  15. 15. Visualization 15
  16. 16. InAirMaS – Enhanced Visualization of Tail Rotation  16 On‐time flight Delayed flight Early flight Cancelled flight Diverted flight Non‐revenue flight Line‐of‐flight (LOF) satisfying crew regulatory requirements  Scheduled maintenance check (M‐H) Base maintenance opportunity (M‐H) Line maintenance opportunity (M‐H)
  17. 17. 17 Maintenance Workload per Tail and per Day/Station Maintenance Workload per tail per site Maintenance Workload per tail per day Risky workload: there is not enough resource to perform it
  18. 18. 18 Network Traffic Analysis MX: Maintenance Traffic density between pair cities at the current moment
  19. 19. 19 Operational Buffers for Aircraft Swapping Predecessor and successor connections to/from a specific flight leg for recovery planning (e.g., A/C swapping)
  20. 20. Long‐term Maintenance Packaging and MRO Forecasting 20 Package for specific tail at specific time period Average required labour‐hour  to maintain the fleet (in‐house) Assumptions: • Fleet Size: 25 • maintenance policy: Equalized • Labour-hour requirements per Week: 359 ~ 79 • Labour Efficiency: 0.75 • PREP rate for ground operations: 20% • QC rate: 10% • Task yield flexibility: 5% (of interval) • Annual utilization: 2500 fh Maximum requirements Subcontracted manpower Open capacity
  21. 21. BOMBARDIER PROPRIETARY INFORMATION21 • Key Definitions: o Maintenance Opportunity (MXO): occurs when an airplane spends a sufficiently long period at a right maintenance station and right time, whether or not maintenance is actually performed. o Maintenance Misalignment (MXMIS): the gap between maintenance demand and available Maintenance Opportunity (in man‐hours) for given tail and time span. • Problem Statement: finding out an aircraft rotation with minimum MXMIS • Ideal Solution: Integrated Aircraft Rotation and Maintenance scheduling: create the route of each aircraft and simultaneously track it for various maintenance tasks using various flight attributes, resulting in a complex mathematical model. • Practical Solution: interactive approaches • Chicken or Egg Dilemma: First aircraft rotation then maintenance scheduling or vise versa? Problem Statement
  22. 22. Experimental Analysis (Fleet Utilization Optimization thru. Simulation) 22
  23. 23. Case Study 23 • Case Study: A Canadian airline over expansion period • Time span: 30 weeks • Planning Horizon: Weekly • Fleet Type: BA01 • Fleet size: 13 → 19 • Flight schedule size: 600 → 800 (flights per week) • Covered cities: 18 → 21 • Frequent MX Tasks: 36FH, 50FH, (n×100)FH; n =1,2,…, 6. • # of Heavy MX sites: 1 • # of Line MX sites: 8
  24. 24. BOMBARDIER PROPRIETARY INFORMATION24 InAirMaS Performance Verification Key Performance Criteria: • MXMIS • Fleet Utilization variability (FUV): To reduce the MXMS, the InAirMaS staggers the aircraft utilization to increases the aircraft availability at maintenance bases; leading to an increased FUV. • InAirMaS gradually mitigates the MXMIS and, at the same time, converges to a stable routing. • Improvement on MXMS using InAirMaS follows the exponential distribution with parameter  = 16.22 o Average improvement = 6%; o maximum improvement ≤ 18%
  25. 25. BOMBARDIER PROPRIETARY INFORMATION25 Financial Savings on Labour‐hours and MTC Visits  • QQ (quantile‐quantile) Plot: is used to determine if two data sets come from populations with a common distribution. By a quantile, we mean the fraction (or percent) of points below a given value (Inverse of c.d.f) • Note: MXMIS = Unsatisfied demand that must be performed as early maintenance = Additional maintenance visits and activities over a long term. • Due to the peaks in the maintenance workload caused by coinciding the maintenance checks having multiplier intervals (e.g., when 200FH task is due, all 50, 100, and 200FH tasks should be done at the same check • Saved labor hours  EXP = 0.0024  EXP = 0.0004 • Reduced Maintenance Visits  EXP = 0.0056 • Expected saving on labor hours = 500 (m‐h) per year • Expected reduction in maintenance visits per aircraft = 10 per year • Expected increase in aircraft availability = 15 block hours per year (average block hour per flight = 1.5 hours) • Expected total annual savings using InAirMaS = $1.5M 95% 5% QQ Plot: Saved labor hours QQ Plot: Reduced MX Visits
  26. 26. Financial Savings on Non‐revenue Flights and Burned Fuel  26 Perfect World: without unexpected interruptions (Cancellations and Diversions) Estimated cost for each non-revenue flight for Q400 fleet: $8000 Number of non‐rev. flights over 8‐month simulation: • Reliability Monthly Report: 149 • Historical data (under real world) : 164 • InAirMaS (under real world): 84 • InAirMaS (under perfect world): 2 Annual cost saving due to non‐revenue flights using InAirMaS  $540K
  27. 27. Fleet Utilization Improvement – Increased Fleet Availability 27 A/C Turn-times = 45 min. MXO: maintenance Opportunity The schedule cannot be flown by less than 16 A/C 18 A/C: • Average daily block time = 9.44 • MXO per tail per week = 33 hours 17 A/C: • Average daily block time = 9.51 • MXO per tail per week = 28 hours • 6FH more fleet availability 16 A/C (minimum): • Average daily block time = 9.58 • MXO per tail per week = 24 hours • 12FH more fleet availability
  28. 28. Fleet Utilization Improvement – Potential Revenuer Flights 28 Net Revenue per flight = $8,347 = $0.19Yield  78seats  0.816PaxLoad  690mile Yield (revenue per revenue passenger mile)  19 cents (WestJet report – May 2015) Using InAirMaS and 18 A/C: • 164 more potential flights (increased frequency) greater than 1 flight hour between 8:00 am and 10:00 pm • Aircraft T/R = 45 min. • Average aircraft usage rate per day will increase from 9.44fh to 11.44fh • Potential net revenue per week = $1,335,000 ORG DST DEPT Weekday D P 3/3/2015 10:15 3 (Wed) D P 3/6/2015 10:15 6 D P 3/7/2015 10:15 7 D P 3/4/2015 10:15 4 D P 3/2/2015 10:15 2 D P 3/5/2015 10:15 5 E B 3/3/2015 22:20 3 E B 3/4/2015 22:20 4 E B 3/5/2015 22:20 5 E B 3/2/2015 22:20 2 K L 3/7/2015 15:25 7 B J 3/8/2015 9:00 1 B J 3/7/2015 9:00 7 B C 3/7/2015 14:20 7 I D 3/2/2015 17:35 2 I D 3/5/2015 17:35 5 P Q 3/3/2015 8:15 3 P Q 3/4/2015 17:30 4 P Q 3/7/2015 8:15 7 P Q 3/3/2015 17:30 3 Potential flight
  29. 29. Efficiency Improvement – Swift Re‐planning
  30. 30. Thank You Q&A 30
  31. 31. BOMBARDIER PROPRIETARY INFORMATION31 QQ ‐ Plot Quantiles are cut‐points dividing a set of observations into equal sized groups QQ Plot: a graphical (non‐parametric) method for comparing two probability distributions by plotting their quantiles against each other. Decile Outlier • Determine sample size N where a desired confidence bounds width (usually for normal Dist.) or bounds ratio (usually for Lognormal Dist.) is satisfied. • Generate sample data , , … , ‐ e.g., time between failures • Calculate Empirical c.d.f # • Select the target distribution (e.g., Weibull) with c.d.f by using comparing histograms and create null hypothesis: : • Fitting: estimate the distribution parameters using an appropriate method (e.g., maximum likelihood) • Verify the goodness of fitting: • QQ‐plot: determine distribution quantiles , , … , using cutpoints ⁄ ; 1, … , where . Draw plot versus sample quantiles . • Kolmogorov‐Smirnov statistic: the hypothesis test will be rejected if is too large.

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