Cancellation Disruption Index Tool CanDIT

1. Cancellation Disruption Index Tool (CanDIT) Mona Kamal Mary Lee Brittlea Sheldon Thomas Van Dyke Bedis Yaacoubi Sponsor: Center for Air Transportation Systems Research (CATSR) Sponsor Contact: Dr. Lance Sherry George Mason University May 9, 2008

2. Overview Problem Background Problem Statement Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

3. Why this Project? Problem Solution Data Connectivity Factors Passenger Factors Disruption Index Analysis Solver Conclusion

4. Background Explain each step Water fall process Complex problemExplain each step Water fall process Complex problem

5. Background Low percentage/ Still is more common than it s suspected Don�t spend too much time on Low percentage/ Still is more common than it s suspected Don�t spend too much time on

6. Possible Cancellation Scenarios Flight cancellation due to mechanical problems Cancellation initiated by the Airlines Flight cancellation due to arrival restrictions, Cancellation initiated by the Air Traffic Control Flight cancellation due to safety restrictions, Cancellation initiated by the FAA Scope for this Problem: ONLY two scenariosScope for this Problem: ONLY two scenarios

7. Scenario1:Flight cancellation due to mechanical problems

8. Scenario 2:Flight cancellation due to arrival restriction AADC: Airport Arrival Demand ChartAADC: Airport Arrival Demand Chart

9. Bedis, make sure you explain how the GUI is used and what the AADC is Bedis, make sure you explain how the GUI is used and what the AADC is

10. Problem Statement Our study does not include any cost analysis, maybe we need to change this to customersOur study does not include any cost analysis, maybe we need to change this to customers

11. Vision Statement A more sophisticated strategy for schedule recovery is needed to aid the controllers� decisions and therefore avoid unnecessary costs to the airline. Once this system is implemented, controllers will have access to an automated decision support tool allowing them to reach low disruption cancellation decisions. Airline Operations controllers (AOC) adjust flights schedules dynamically throughout the day. This adjusting process is, however, based to a great extent on controllers� intuitions and acquired domain knowledge. Airline Operations controllers (AOC) adjust flights schedules dynamically throughout the day. This adjusting process is, however, based to a great extent on controllers� intuitions and acquired domain knowledge.

12. Scope Our focus is on two factors which lead to disruption : The affect a canceled flight could have on other flights the same day The reassignment of passengers on a canceled flight to other flights We are considering disruption caused to ONLY the current day's schedule

13. The Approach

14. The team has �  Considered a single airline as the initial focus Looked at a one day flight schedule Determined connectedness of flights to one another Calculated a passenger reassignment factor Developed a disruption index which incorporates the effects of connectedness and passenger mobility Created a tool, which uses these indices to determine the lower disruption flight(s) to cancel

15. Disruption Index End result Decision making tool A numerical value rating the disruption that the cancellation of a flight will cause to the airline for the remainder of the day Combination of two factors: Connectivity Factors Passenger Factors Emphasize the second point and make sure it is understood by the audience. I think it is really important the audience understands exactly what we are trying to get in the end so that they can follow the rest of the presentationEmphasize the second point and make sure it is understood by the audience. I think it is really important the audience understands exactly what we are trying to get in the end so that they can follow the rest of the presentation

16. Basis of our work

17. Data A spreadsheet was provided by the Study Sponsor containing the flight schedules of all domestic flights for one day Information on all flights including: Carrier and tail number (i.e. airplane ID) Origin city and arrival city Scheduled departure and arrival times Actual departure and arrival times Our sponsor provided us with the information we needed in the form of a spreadsheet. The spreadsheet contains a day long schedule of departures and arrivals for all domestic airlines. The information included�(you can read off the list of info)Our sponsor provided us with the information we needed in the form of a spreadsheet. The spreadsheet contains a day long schedule of departures and arrivals for all domestic airlines. The information included�(you can read off the list of info)

18. This space time diagram shows how flights can interact throughout the day. The x-axis is the time of day and the y-axis is the airport code. Each line represents a plane and its movement during the dayThis space time diagram shows how flights can interact throughout the day. The x-axis is the time of day and the y-axis is the airport code. Each line represents a plane and its movement during the day

19. Statistics Airline A Fleet consists of more than 500 aircraft Most are Boeing 737 aircraft Each aircraft flies an average of 7 flights per day, totaling 13 flight hours per day Serves 64 cities in 32 states, with more than 3,300 flights a day We chose a specific airline to base our work on. This chosen airline has a fleet consisting of over 500 aircrafts. Each aircraft flies an average of 7 flights per day and 13 hours. The airline serves 64 cities, in 32 states, with more than 3300 flights per day. We chose a specific airline to base our work on. This chosen airline has a fleet consisting of over 500 aircrafts. Each aircraft flies an average of 7 flights per day and 13 hours. The airline serves 64 cities, in 32 states, with more than 3300 flights per day.

20. First Step: Connectivity Our first step was to calculate the connectivity factorsOur first step was to calculate the connectivity factors

21. Flight Connectivity Definition: The transfer of passengers, crew, or aircraft from arriving at one destination to departing to the next within a designated time window We defined connectivity as:�(you can read the definition)We defined connectivity as:�(you can read the definition)

22. This graph clarifies our definition of connectivity based on the connection window. Give the example of the flight from IND arriving at the beginning of the first connection window and how everything that departs within that 2 hour window would be considered a connecting flight This graph clarifies our definition of connectivity based on the connection window. Give the example of the flight from IND arriving at the beginning of the first connection window and how everything that departs within that 2 hour window would be considered a connecting flight

23. Connectivity Factors (CFs) Connectivity factors determines the number of down-path flights that could be impacted by the cancellation of a single flight Each flight leg is assigned a connectivity factor Using our definition of connectivity, we developed a connectivity factor which determines the number of down-path flights that could be impacted by the cancellation of a single flight Each leg is assigned a connectivity factor based on the number of connecting flights. This tree shows how one flight could be connected to multiple flights throughout the day and up with a fairly large connectivity factorUsing our definition of connectivity, we developed a connectivity factor which determines the number of down-path flights that could be impacted by the cancellation of a single flight Each leg is assigned a connectivity factor based on the number of connecting flights. This tree shows how one flight could be connected to multiple flights throughout the day and up with a fairly large connectivity factor

24. 100% Flight Connectivity Arriving flights connect to all flights that are scheduled to depart from that airport within a designated connection window. Assumptions: [1]: There is at least one passenger or crew member on an arriving flight that will have to board a departing flight. [2]: Connecting flights must be assigned a minimal time for passengers to physically transfer from the arriving flights. We started off assuming that all flights are connected with 100% connectivity. This means that any flight departing within the connection window time is considered to be a connecting flight. We assumed that at least one passenger or crew member would board one of the departing flights. We set a minimum connection time to allow for passenger transfers and didn�t consider flights that were earlier than that minimum connection time.We started off assuming that all flights are connected with 100% connectivity. This means that any flight departing within the connection window time is considered to be a connecting flight. We assumed that at least one passenger or crew member would board one of the departing flights. We set a minimum connection time to allow for passenger transfers and didn�t consider flights that were earlier than that minimum connection time.

25. For example, take this system of interacting flightsFor example, take this system of interacting flights

26. This flight departing from BWI has a connectivity factor of 7. We count the number of legs that this flight is connected to throughout the day. The highlighted tree diagram shows how this one flight connects to 7 other flights throughout the day This flight departing from BWI has a connectivity factor of 7. We count the number of legs that this flight is connected to throughout the day. The highlighted tree diagram shows how this one flight connects to 7 other flights throughout the day

27. We start counting from the end of the day. We start from the last two flights in the system which are labeled here 1�We start counting from the end of the day. We start from the last two flights in the system which are labeled here 1�

28. �and 2�and 2

29. Connected to these two legs is leg number threeConnected to these two legs is leg number three

30. And then leg number 4And then leg number 4

31. Leg 5 has the same tail number as the original flight so it is connected to itLeg 5 has the same tail number as the original flight so it is connected to it

32. Leg 6 is connected to leg 3, 4 and 5. It is only counted once though, we don�t have duplications in our caluclationsLeg 6 is connected to leg 3, 4 and 5. It is only counted once though, we don�t have duplications in our caluclations

33. Finally, the sum of all these legs together, which are connected to the flight departing from BWI, are what produce this connectivity factor of 7 Finally, the sum of all these legs together, which are connected to the flight departing from BWI, are what produce this connectivity factor of 7

34. 100% flight connectivity [45min,120min] Based on the graph of Connectivity Factors vs. Arrival Time, the number of connecting flights generally decreases as it gets to be later in the dayBased on the graph of Connectivity Factors vs. Arrival Time, the number of connecting flights generally decreases as it gets to be later in the day

35. 100% connectivity: Sensitivity Analysis The connection window was varied over 5 more time intervals: [45* min, 120 min] [45 min, 150 min] [45 min, 180 min] (Baseline) [45 min, 210 min] [45 min, 240 min] *The minimal time window was fixed at 45 minutes for this study, as a reasonable amount of time for physical transfer of passengers Out intent for varying the connectivity was to determine which connectivity window most accurately depicts reality (needs to be better explained)Out intent for varying the connectivity was to determine which connectivity window most accurately depicts reality (needs to be better explained)

36. BKS: I think maybe we should use 180 vs. 210 instead of 240 vs. 150, since we ended up deciding on the 180 min connection window How these affect our calculations -Adding more to the interval could change trends slightly, but only for a few factors 180 vs. 150: TPA to JAX leave 7:15, arrive 8:05 (increases from 1 to 310), also wide range along y = x line 210 vs. 180: Biggest Increases FLL JAX 6:35 ,7:40 (275 to 406) BWI SDF 7:10 8:50 (428 to 578) 240 vs. 210: BWI ORF 7:20 8:10 (1, 189) BKS: I think maybe we should use 180 vs. 210 instead of 240 vs. 150, since we ended up deciding on the 180 min connection window How these affect our calculations -Adding more to the interval could change trends slightly, but only for a few factors 180 vs. 150: TPA to JAX leave 7:15, arrive 8:05 (increases from 1 to 310), also wide range along y = x line 210 vs. 180: Biggest Increases FLL JAX 6:35 ,7:40 (275 to 406) BWI SDF 7:10 8:50 (428 to 578) 240 vs. 210: BWI ORF 7:20 8:10 (1, 189)

37. -Adding more to the interval could change trends slightly, but only for a few factors 180 vs. 150: TPA to JAX leave 7:15, arrive 8:05 (increases from 1 to 310), also wide range along y = x line 210 vs. 180: Biggest Increases FLL JAX 6:35 ,7:40 (275 to 406) BWI SDF 7:10 8:50 (428 to 578) 240 vs. 210: BWI ORF 7:20 8:10 (1, 189) -Adding more to the interval could change trends slightly, but only for a few factors 180 vs. 150: TPA to JAX leave 7:15, arrive 8:05 (increases from 1 to 310), also wide range along y = x line 210 vs. 180: Biggest Increases FLL JAX 6:35 ,7:40 (275 to 406) BWI SDF 7:10 8:50 (428 to 578) 240 vs. 210: BWI ORF 7:20 8:10 (1, 189)

38. Realistically, flights are connected at different rates based on the airline strategy (hub and spoke or focus cities �), the connecting airport , and other factors. A study led by Darryl Jenkins on Airline A developed % passengers connectedness at all airports. The data used in the study: Average Outbound, non interline passengers (Pax) from each city (from O & D Database) Average enplaned Pax from each city (from the Onboard Database) Be sure to define enplaned as presenting Be sure to define enplaned as presenting

39. Airport Percent Connect Year of 2002 Data Author divides airports to : Major connecting airports Partial Connecting airports Non-connecting airports Explain this in detail since it seems to confuse the audience Explain this in detail since it seems to confuse the audience

40. Flight Connectedness We then incorporated the Airport Percent Connect (APC) data to our CF generator algorithm: if APC >= 15 % , then 100% connect if APC < 2%, then 0 % Connect if 2%<APC<15%, then [(APC- 2) * 100 / 13 ] % Connect

41. Comparing Graphs from the two methods

42. Comparing APC and 100% Connectivity The fact that there is variance, and not straight line, means that its� [substitute graph with larger fonts]The fact that there is variance, and not straight line, means that its� [substitute graph with larger fonts]

43. Comparing results from the two methods With 100% connectivity, there is a difference of 42 (17%) in the connectivity factors Destined for PDX and SEA With the revised APC, there is a difference of 9 (8%) With 100% connectivity, there is a difference of 42 (17%) in the connectivity factors Destined for PDX and SEA With the revised APC, there is a difference of 9 (8%)

44. Algorithm on other airlines Different airlines function differently, hub and spoke, focus cities� Shows that regardless they have similar connectivity factor trendsDifferent airlines function differently, hub and spoke, focus cities� Shows that regardless they have similar connectivity factor trends

45. The next step in calculating the disruption indices is the passenger factorThe next step in calculating the disruption indices is the passenger factor

46. Passenger Factor Takes into consideration number of passengers on flight as well as remaining seats that day Equation: Higher penalty for a higher ratio * Bulk of project was spent on coming up with connectivity factors, what was asked of us initially, most time consuming. * Pax Factors easier to calculate and was a deciding factor that was added on further into our project * This factor takes into consideration the number of passengers on a flight and the number of additional seats available throughout the day on flights with the same flight pattern as the one being considered The idea is to make sure if you cancel a flight that you have enough seat later on in the day to reassign the passengers to other flights The ratio used as the penalty takes the number of passengers on the flight in the numerator and all the seats available throughout the day on similar flights in the denominator * Ideally less than one, would want there to be enough or more available seats to accommodate passengers on a canceled flight. Higher penalty for less available seats* Bulk of project was spent on coming up with connectivity factors, what was asked of us initially, most time consuming. * Pax Factors easier to calculate and was a deciding factor that was added on further into our project * This factor takes into consideration the number of passengers on a flight and the number of additional seats available throughout the day on flights with the same flight pattern as the one being considered The idea is to make sure if you cancel a flight that you have enough seat later on in the day to reassign the passengers to other flights The ratio used as the penalty takes the number of passengers on the flight in the numerator and all the seats available throughout the day on similar flights in the denominator * Ideally less than one, would want there to be enough or more available seats to accommodate passengers on a canceled flight. Higher penalty for less available seats

47. Passenger Factor No data available on number of passengers and capacity of individual flights Formula fully functional so airline can input flight information For analysis purposes, used a random number generator No data available on actual capacity of flight or number of passengers on flight. For analysis, decided to generate random numbers with in a specific range. We researched averages on the capacities of airlines and percent occupancy and used those factors to generate random numbersNo data available on actual capacity of flight or number of passengers on flight. For analysis, decided to generate random numbers with in a specific range. We researched averages on the capacities of airlines and percent occupancy and used those factors to generate random numbers

48. With those two factors we are able to calculate the disruption indexWith those two factors we are able to calculate the disruption index

49. Calculation of Disruption Index Disruption Index = W1(ConnFact) + W2 (a)(PaxFact) W1 and W2 = Weights given to each factor (a one time setting for each airline) a = Scaling factor for passengers Disruption index is sum of weighted factors (connectivity and passenger) The weights are values given by the airline depending on how much emphasis they put on each one of those factors The passenger factor is multiplied by a scaling factor since the range of values for each of the two factors was so different. This makes them more comparable Decided to use scale of Connectivity factors rather than scaling it down to a 0-1 number because whole numbers are more pleasing to the eye than decimals and may loose accuracy if a big range Disruption index is sum of weighted factors (connectivity and passenger) The weights are values given by the airline depending on how much emphasis they put on each one of those factors The passenger factor is multiplied by a scaling factor since the range of values for each of the two factors was so different. This makes them more comparable Decided to use scale of Connectivity factors rather than scaling it down to a 0-1 number because whole numbers are more pleasing to the eye than decimals and may loose accuracy if a big range

50. Spreadsheet Solver Initially was planning on using an integer program to come up with an optimal solution. Good tool, but may produce infeasible solutions if constraints too restrictive, only one solution, optimal� Decided instead to use a spreadsheet solver and incorporate the constraints produced in the IP The solver shows the flights that match the specified constraints and the DI for each. It ranks them based on DI (from lowest to highest) and shows the user the optimal solution, as well as all other feasible solutions. Including those that may be slightly less than optimal Initially was planning on using an integer program to come up with an optimal solution. Good tool, but may produce infeasible solutions if constraints too restrictive, only one solution, optimal� Decided instead to use a spreadsheet solver and incorporate the constraints produced in the IP The solver shows the flights that match the specified constraints and the DI for each. It ranks them based on DI (from lowest to highest) and shows the user the optimal solution, as well as all other feasible solutions. Including those that may be slightly less than optimal

51. How it All Works

52. Functionality Test Algorithm tested for functionality using historical data Different airlines tested, each with different schedule date Shows how airline would use this data Pretty much read of bullets, but don�t really read them ?Pretty much read of bullets, but don�t really read them ?

53. For this first airline, the constraints set were to consider flights departing from MSP between 9 and 11 AM. The flights were then sorted from lowest to highest DI. In this case, based solely on the constraints, the airline would have more than 30 flights to consider for cancelation. This algorithm narrows it down to three optimal values to pick from. They also have the option of considering flights that are close to optimal (with DI of 2) and would increase their choices to 7. The DI�s range between 1 and 35, with the majority of them being between 1-8. You can also see that the last two flights with the highest DI�s would greatly disrupt the system if canceled and should therefore be avoidedFor this first airline, the constraints set were to consider flights departing from MSP between 9 and 11 AM. The flights were then sorted from lowest to highest DI. In this case, based solely on the constraints, the airline would have more than 30 flights to consider for cancelation. This algorithm narrows it down to three optimal values to pick from. They also have the option of considering flights that are close to optimal (with DI of 2) and would increase their choices to 7. The DI�s range between 1 and 35, with the majority of them being between 1-8. You can also see that the last two flights with the highest DI�s would greatly disrupt the system if canceled and should therefore be avoided

54. For this airline, flights departing from CLT between 8:00 and 10:00 AM. Again the airline is given more than 30 flights to choose from. The DI�s for these flights have a greater range, between 12 and 189, but there is only one optimal solution. The airline could choose that one airline, or consider other flights with DI�s close to optimalFor this airline, flights departing from CLT between 8:00 and 10:00 AM. Again the airline is given more than 30 flights to choose from. The DI�s for these flights have a greater range, between 12 and 189, but there is only one optimal solution. The airline could choose that one airline, or consider other flights with DI�s close to optimal

55. Solving Tool

56. Tom�s Solver hyperlink

57. Solving Tool

58. Conclusions Created an index that assigns a numerical value based on the degree of disruption in the system Developed a tool to allow controllers to make better informed decisions Tool can be easily modified to incorporate factors not previously considered Tool will allow users to make an educated decision based on the disruption of a flight Reduces time to make decision and may improve customer satisfaction

59. Future Works Consider crew connectivity Consider other factors in disruption index not previously considered (such as cost) Consider flight interconnectivity Consider linking tool to web to attain real time data Considering more than just a single day schedule

60. References

61. Question

62. Backup-Varying Connection windows For most flights, expanding the connection window, did not make a great difference in the connectivity factorFor most flights, expanding the connection window, did not make a great difference in the connectivity factor

63. Investigating Connectedness-Sensitivity . These drastic increases all occurred at airports with less flights. This correlation shows the enormous trend that flight frequency plays in connectivity. This is in contrast to flights to MDW which show an average increase of 13% in connectivity when the connection window is expanded by 30 minutes.. These drastic increases all occurred at airports with less flights. This correlation shows the enormous trend that flight frequency plays in connectivity. This is in contrast to flights to MDW which show an average increase of 13% in connectivity when the connection window is expanded by 30 minutes.

64. Airport Percent Connect CFs

65. EVM

66. WBS

67. GANNT

69. Window chosen for analysis For analysis purposes, chose [45 min, 180 min] The airline may choose a connectivity window which fits their flight patterns best The time window is an appropriate cut-off because the values �

70. Generalizing Algorithm Data for two more airlines has been compiled Connectivity factors have been computed Airports differ for each airline Partial-connection percentages have only been found for the first airline (Airline A) Known airports have been assigned same connection percentage as from the first airline Unknown airports have been given a default connection percentage Rename?Rename?

71. The fact that the graphs behaved similarly to Airline A when partial connectivity was incorporated helps us verify that this general method should work for other airlines and can be used as a general tool.The fact that the graphs behaved similarly to Airline A when partial connectivity was incorporated helps us verify that this general method should work for other airlines and can be used as a general tool.

72. Agents/Stakeholders Airline Operations Control FAA Air traffic controllers Passengers Pilots/flight crew Maintenance crew Air traffic controllers : Determine the capacity FAA: Set the rules Passengers : most directly affected Air traffic controllers : Determine the capacity FAA: Set the rules Passengers : most directly affected