1. MIT ICAT ICATMIT M I T I n t e r n a t i o n a l C e n t e r f o r A i r T r a n s p o r t a t i o n Virtual Hubs: A Case Study Michelle Karow karow@mit.edu John-Paul Clarke johnpaul@mit.edu MIT ICAT ICATMIT

2. MIT MIT Presentation Overview: Motivation Definition Characteristics Problem formulation Application at a major US carrier Limitations and future considerations

3. MIT ICAT ICATMIT Irregular operations at a hub airport can be crippling to an airline schedule Reduction in capacity typically necessitates cancellations and delays Effects resonate network-wide and on all levels of operation (fleet, maintenance, crew and passengers) Majority of irregularities caused by weather Could airlines reduce the number of delays and cancellations by re- routing entire connecting banks to an airport with excess capacity?

4. MIT ICAT ICATMIT Re-directing flights through a virtual hub can provide relief to the original hub with minimal disruption Definition: A virtual hub is a predetermined alternative airport that during irregular operations at the original hub, hosts connection complexes to maximize passenger flow through the network. Shift connecting demand over two hubs, decreasing strain on the original hub Continuity of passenger flow, insuring a reduction in total passenger delay Capitalize on under-utilized airports

5. MIT ICAT ICATMIT Origin Original Hub Virtual Hub Destination Passengers destined for the hub Origin Destination Passengers connecting to destinations not served by the virtual hub Sample virtual hub network

6. MIT ICAT ICATMIT Virtual hubs can be identified by the following characteristics: Low average daily delays Check FAA’s Airport Capacity Benchmark report for delay rankings of US airports Geographically equivalent location to the original hub Check relative location to existing hub Excess capacity Track airline gate utilization throughout the day, given low delays indicate excess airport capacity Virtual Hub Candidates Virtual Hub Excess Capacity Average Delays Geographical location

7. MIT ICAT ICATMIT Implementing a virtual hub network consists of two phases: The Virtual Hub Model and The PRM Disrupted Passengers Virtual Hub Model Passenger Re-accommodation Module (PRM) Passengers that cannot be accommodated Passengers that can be re- accommodated (and itineraries) Add to the next time window Accommodated Passengers

8. MIT ICAT ICATMIT Phase I: Implementing a virtual hub network Implemented in the hours before the weather is predicted to impact the operations at the original hub Maximizes passenger flow, in turn minimizing total passenger delay Solved iteratively over connecting bank time- windows until weather has cleared Maximize Passenger Flow Time Window t 1 …. Airport Capacities Passenger Itineraries Original Flight Schedule Aircraft Capacities Original Hub Flights Virtual Hub Flights Adjusted Itineraries Delayed/ Cancelled Flights Anticipated Weather/ Ground Delay Program Update Variables for Next Time Window Maximize Passenger Flow Time Window t 2 Maximize Passenger Flow Time Window t n

9. MIT ICAT ICATMIT Key Assumptions: Ground resource availability Crew and maintenance flexibility Passenger connections within a time window Passenger consent

10. MIT ICAT ICATMIT The virtual hub model is formulated as a mixed integer network flow problem. Input data: Size of the time windows Passenger itineraries Original flight schedules Airport capacities Aircraft capacities

11. MIT ICAT ICATMIT Objective function: Maximize passenger flow Where: Oset of origins Dset of destinations Hset of hub airports {OH, VH, VH s } d ij demand from origin i to destination j z ijk positive variable representing the fraction of demand traveling on the network from origin i to destination j through hub k

12. MIT ICAT ICATMIT Subject to: Definition of z ijk : A path exists from origin to destination through a hub Where: w ijk binary decision variable that the network exists from origin i to destination j through hub k x ik binary decision variable that the network exists from origin i to hub k y kj binary decision variable that the network exists from hub k to destination j

13. MIT ICAT ICATMIT Subject to: Airport capacity: Upper bounds on aircraft sent to a hub Where: x ik binary decision variable that the network exists from origin i to hub c k capacity of hub k

14. MIT ICAT ICATMIT Subject to: Aircraft Capacity: Upper bounds on the number of passengers on an aircraft Where: d ij demand from origin i to destination j z ijk binary decision variable that the network exists from origin i to destination j through hub k p i, q j aircraft capacity to and from the hub, respectively f i,, g j excess aircraft capacity on scheduled flights to and from the virtual hub, respectively

15. MIT ICAT ICATMIT Subject to: Hub choice: A flight is served either by the virtual hub or the original hub Conservation of Flow: Upper bounds on aircraft departures from hubs Where: x ik binary decision variable that the network exists from origin i to hub k y kj binary decision variable that the network exists from hub k to destination j b k number of aircraft on the ground from the previous time window at hub k

16. MIT ICAT ICATMIT Phase II: Re-accommodating disrupted passengers After the scheduling decisions are made for a time window, some passengers will be disrupted and require re-accommodation. Disrupted passengers for the virtual hub network include the following: A connecting passenger with their original flight from their origin serviced by the virtual hub and their original flight to their destination serviced by the original hub. A connecting passenger with their original flight from their origin serviced by the original hub and their original flight to their destination serviced by the virtual hub. A non-stop passenger with their original flight either to or from the original hub serviced by the virtual hub.

17. MIT ICAT ICATMIT An overview of the Passenger Re-accommodation Module (PRM) Disrupted Passengers from Virtual Hub Model Re-accommodated Passengers 2 - leg itinerary 1 - leg itinerary 1 st Leg diverted to VH 2 nd Leg rescheduled from VH Originating at OH Destined for OH Accommodated on a later flight from OH Accommodated on a later flight to OH Accommodated on a later flights through OH Accommodated on a later flight from VH Accommodated on a later flights through OH Accommodated on a later flight to VH 1 st Leg on VH s + 2 nd leg rescheduled from VH 1 st Leg diverted to VH + 2 nd leg on VH s

18. MIT ICAT ICATMIT A closer look: Application of the Virtual Hub Network to a Major US Carrier A thunderstorm was present at the original hub airport on March 9, 2002 while the virtual hub remained relatively unaffected. For this day, throughout the network: Domestic and International Flights4,000 Number of Passengers99,000 Distinct Itineraries38,000

19. MIT ICAT ICATMIT Major delays plague the original hub while relatively minor effects are felt at the virtual hub Delayed Flights per Hub on March 9, 2002

20. MIT ICAT ICATMIT Input data: Size of the Time Window Average Connection Time151 minutes Highest Frequency Markets 1 flight per 60 minutes Size of the Time Window120 minutes The two-hour time window was selected to accommodate both the need for high scheduling accuracy and a large percentage of passengers connecting in distinct time windows.

21. MIT ICAT ICATMIT Input data: Passenger Itineraries ItinerariesPassengers Traveling through the original hub during the period of irregular operations 4,34219,291 Only the flight legs originating or arriving at the original hub were considered. Itineraries with international flight legs were treated as originating or arriving at the original hub Itineraries with connections overlapping two time windows were separated into two itineraries, originating and arriving at the original hub

22. MIT ICAT ICATMIT Input data: Original Flight Schedules DomesticInternational Flights between 8am and 6pm at the original hub 54846 Only domestic flights are eligible for diversion to the virtual hub International flights operated by the airline are assumed to depart or arrive within one time window of their schedule. International flights operated by the airline’s code-share partners are also assumed to depart or arrive within one time window of their schedule.

23. MIT ICAT ICATMIT Input data: Virtual Hub Airport Capacities Track cumulative operations at the virtual hub airport throughout the day Bias the data to produce positive aircraft totals at the airport throughout the day (account for aircraft kept overnight) Subtract the number of operations at the airport from the number of gates to find the excess capacity per time window

24. MIT ICAT ICATMIT Throughout the day, the virtual hub is does not reach it’s maximum gate capacity of 45 gates Cumulative Number of Aircraft for the Airline at the VH on March 9, 2002

25. MIT ICAT ICATMIT Subtracting the cumulative number of aircraft from the total number of gates provides a measure of excess capacity Excess Capacity for the Airline at the VH on March 9, 2002

26. MIT ICAT ICATMIT The excess capacity over the day is compressed into two hour time windows to determine the VH excess capacity during irregular ops Excess Capacity for the Airline at the VH on March 9, 2002

27. MIT ICAT ICATMIT Input data: Virtual and Original Hub Airport Capacities Time Window Scheduled Domestic Arrivals Scheduled Domestic Departures c OH : Original Hub Capacity c vh : Virtual Hub Capacity 800 to 100035572119 1001 to 120041582819 1201 to 140047423219 1401 to 160059534019 1601 to 180033372219 The capacity at the original hub was reduced by 1/3 to reflect the reduction in the airport arrival rate required by the ground delay program. The capacity at the virtual hub was the minimum number of gates to accommodate all diverted flights.

28. MIT ICAT ICATMIT Input data: Aircraft Capacities Flights remain assigned to their originally schedule aircraft, regardless of which hub airport they are sent to. Capacity for flights traveling through the original hub is the number of seats on the aircraft. Capacity for scheduled flights through the virtual hub is the number of seats minus the number of passengers booked on the flight (i.e., excess capacity).

29. MIT ICAT ICATMIT Phase I Implementation: The Virtual Hub Model Time Window Number of Passengers ConstraintsVariables Passengers Served (Objective Function) 800 to 10004,43626,30412,2474,037 1001 to 12006,19131,31114,5665,747 1201 to 14005,13926,01912,1124,753 1401 to 16006,29841,10019,0995,852 1601 to 18003,12216,6397,7622,978 Solution times for the time windows range from 5 minutes to over an hour, depending on the sparsity of the data set. In each time window, the maximum number of aircraft were sent to the original hub.

30. MIT ICAT ICATMIT Phase II Implementation: PRM Time Window Passengers Not Accommodated by Virtual Hub Model Re-accommodated Passengers Disrupted International Passengers Un-accommodated Passengers 800 to 1000399340536 1001 to 120044432110716 1201 to 1400386361214 1401 to 16004463565832 1601 to 180014413194 Passengers (and itineraries) not accommodated by the virtual hub model were entered into the PRM after each time window. International passengers were considered disrupted if their domestic leg was delayed by more than 4 hours (i.e., two time windows). Un-accommodated passengers are passengers that could not be accommodated by the end of the day on flights traveling through either hub airport.

31. MIT ICAT ICATMIT Comparing Actual Recovery to the Virtual Hub Network Actual Recovery Virtual Hub Network Total Passengers19,291 Number of Cancelled Flights1230 Passengers Requiring Re- Accommodation 7741,665 Disrupted International Passengers 237248 Un-Accommodated Domestic Passengers 20767 Passengers Delayed Over Two Hours 14,123838 94% reduction

32. MIT ICAT ICATMIT Limitations and Future Considerations: Number of airline gates is somewhat flexible; cannot ensure airports will maintain good virtual hub candidacy. Crew constraints and contract conditions could limit feasibility and increase diversion costs. Availability of ground resources may constrain the capacity of the virtual hub. Iterating over time windows under-estimates abilities of weather forecasting while optimizing over multiple time windows adds complexity and non-linearity. Consideration of re-accommodating passengers on scheduled non-stop flights will provide a better (or equivalent) solution.