Dynamic and Flexible Airline Schedule Design Cynthia Barnhart Hai Jiang Global Airline Industry Program October 26, 2006
De-banked (or De-peaked) Hubs Depature/arrival activities # of departures/arrivals # of departures/arrivals 20 15 10 5 0-5 -10-15 -20 20 15 10 0 100 5 0 0 100-5 -10-15 -20 200 200 300 300 400 Time Depature/arrival activities 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 departure 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 Time departure arrival arrival 1600 1700 1800 1900 2000 1700 1800 1900 2000 2100 2200 2300 2100 2200 2300 American de-peaked ORD (2002), DFW (2002), MIA(2004) Continental de-peaked EWR United de-peaked ORD (2004), LAX (2005), SFO (2006) Delta de-peaked ATL (2005) Lufthansa de-peaked FRA (2004) Program 2006 2
Opportunity in a De-Peaked Schedule Time HUB MinCT 25min Flight re-timing creates new itineraries, adjusts market supply Program 2006 3
Dynamic Airline Scheduling Dynamic scheduling idea Move the capacity (supply) in various markets so as to optimize profitability in response to demand variability: Retiming flights Creating new itineraries and eliminating itineraries only if no bookings to date Swapping aircraft Re-assigning aircraft within the same fleet family Maintaining crew feasibility Maintaining conservation of flow (or balance) by fleet type Maintaining satisfaction of maintenance constraints Program 2006 4
Case Study Major US Airline 832 flights daily 7 aircraft types 50,000 passengers 302 inbound and 302 outbound flights at hub daily Banked hub operations must de-bank Re-time +/- 15 minutes Re-fleet A320 & A319 CRJ & CR9 One week in August, with daily total demand: higher than average (Aug 1) average (Aug 2) lower than average (Aug 3) Protect all connecting itineraries sold in Period up to d-t t =21 or 28 days Two scenarios concerning forecast demand Perfect information Historical average demand Program 2006 5
Improvement In Profitability Consistent improvement in profitability Forecast A 4-8% improvement in profit 60-140k daily Forecast B 2-4% improvement in profit 30-80k daily Benefits remain significant when using Forecast B-B a lower bound not including benefit from aircraft savings, reduced gates and personnel Increase in Profit 10% 8% 7.63% 6.52% 6.70% 6% 5.09% 4.91% 4.84% 4.35% 4.43% 4.01% 4% 2.55% 2.64% 1.97% 2.02% 1.99% 2% 0% Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Dynamic scheduling under Forecast A Dynamic scheduling under Forecast B Program 2006 6
Comparison: Re-Time & Re-Fleet Average daily profitability results ($) Forecast A Forecast B P B /P A Dynamic Scheduling 99,541 49,991 50.22% Re-fleeting Only 28,031 7,542 26.91% Re-timing Only 44,297 37,800 85.33% The two mechanisms are synergistic P A (Dynamic scheduling) > P A (re-fleeting)+p A (re-timing timing) P B (Dynamic scheduling) > P B (re-fleeting)+p B (re-timing timing) Re-timing is less affected by deterioration of forecast quality Larger P B /P A ratios Re-timing contributes more than flight re-fleeting P A (re-fleeting) < P A (re-timing) P B (re-fleeting) < P B (re-timing) Program 2006 7
Case Study 2: Weekly Schedules Assess the performance of dynamic scheduling under a weekly schedule 1 0.8 Load factor 0.6 0.4 Mean Minimum 10th Percentile Lower Quartile Median Upper Quartile 90th Percentile Maximum 0.2 0 1 8 15 22 Day Program 2006 8
Weekly Schedule Results Schedule Generation Approach A: Extend the daily schedule design model to a weekly model (computationally intractable) Approach B: Generate Monday schedule using average Monday forecast; generate Tuesday schedule using average Tuesday forecast; and so on These schedules do not form a weekly schedule, but are able to take weekly demand variation into consideration Dynamic scheduling continues to improve profitability Average daily profit improvement Daily Weekly Forecast A 99,541 (5.26%) 92,384 (4.97%) Forecast B 49,991 (2.64%) 42,463 (2.28%) Program 2006 9
Other Statistics System load factors went up 0.5-1% Aircraft savings perfect + retime + swap average + retime + swap 1-Aug 1 A320 1 A320 2-Aug 1 A320 1 CR9 1 A320 1 CR9 3-Aug 1 A320 2 CR9 1 A320 Schedule changes About 100 fleet changes 85-90% flights are retimed Average retiming of 8 minutes -10-5 -15 +5 +10 +15 0 Program 2006 10
Flexible Planning Re-optimization decisions constrained by original schedule Can we design our original schedule to facilitate dynamic scheduling? Goal Maximize the number of connections that can be created to accommodate unexpected demands Objective function value within.0% of original schedule Program 2006 11
Preliminary Results Under Forecast A, improvement is not significant When forecast is perfect, don t t need to create a schedule that can be altered to accommodate variations in demand Under Forecast B, improvements obtainable When forecast is imperfect, an improved schedule can be constructed with dynamic scheduling Profit increase comparing to ordinary de-peak model 4.00% 3.00% 2.00% 1.00% 0.00% -1.00% Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 pefect average Program 2006 12
De-Banking and Robust Optimization- No Dynamic Scheduling Schedule A Schedule B Schedule c (banked) (de-banked) (robust de-banked) Revenue 8,170,245 8,146,066 8,165,746 - -0.30% -0.06% Cost 6,001,400 5,929,789 5,929,789 - -1.19% -1.19% Profit 2,168,845 2,216,277 2,235,957-2.19% 3.09% No. of aircraft 171 170 170 Program 2006 13
Summary of Findings Flexible planning and dynamic scheduling result in consistent improvement in Profitability Allows additional revenue capture without additional resources Flight retiming effectively increases the number of connecting passengers served Load factor Number of passengers (connecting/nonstop) served Savings in number of aircraft used Benefit remains significant when the forecast is relatively simple Re-timing decisions more robust to demand uncertainties Program 2006 14
Questions? Program 2006 15