cancellation disruption index tool (candit) mona kamal mary lee brittlea sheldon thomas van dyke...
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Cancellation Disruption Index Tool (CanDIT)
Mona Kamal Mary Lee
Brittlea SheldonThomas Van Dyke
Bedis YaacoubiSponsor: Center for Air Transportation Systems Research (CATSR)
Sponsor Contact: Dr. Lance Sherry
George Mason UniversityMay 9, 2008
Overview• Problem
• Background• Problem Statement
• Solution• Data• Connectivity Factors• Passenger Factors
• Disruption Index• Analysis • Solver• Conclusion
Why this Project?
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
Background
Flight scheduling is a multi-step, water fall process
Fleet assignment
Aircraft maintenance
routing
Flight Schedule
generation
Crew Scheduling
Yield Management
OPERATIONS MANAGEMENT
Background
According to Bureau of Transportation Statistics (BTS)
2003 2004 2005 2006 2007 2008 Average SDAmerican Airline (14.8%)*% Cancelled 1.61 1.78 1.45 1.57 2.83 2.70 1.99 0.61SouthWest (12.2%)*% Cancelled 1.01 1.02 0.85 0.81 0.85 0.80 0.89 0.10United (11.5%)*% Cancelled 1.09 1.18 1.30 2.05 2.43 2.62 1.78 0.67Delta (10.8%)*% Cancelled 1.05 1.56 2.69 1.52 1.37 1.49 1.61 0.56
* Market share based on revenue passenger miles for the year 2007 Average 1.57 %Stdev 0.65 %
258 Domestic Flights Cancelled Per Day
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
Scenario1:Flight cancellation due to mechanical problems
Report a mechanical problem
Provide feedback: Update is received Request the impact of canceling the flight
Provide Disruption Factor of the flight
Request impact of swapping flights
Provide Disruption Factor for potential flights
Provide prioritized cancellation strategy
Provide appropriate decision
PILOT/Maintenance Crew Airline Flight Cancellation Decision Tool
Scenario 2:Flight cancellation due to arrival restriction
Airport Arrival Demand saturation
AADC Airline Flight Cancellation Decision Tool Operations GUI
Request scheduled departing flightsShow list of departing flights
Request Disruption Indices for each departing flight to the low demand airport
Provide Disruptions Indices for each flight
Request prioritized flight cancellation decision Offer the prioritized flight disruptions
Cancel low disruption flight
• Currently, airline operations controllers rely on a Graphical User Interface (GUI) and Airport Arrival Demand Chart (AADC) to decide which flight to cancel.
• Process is time consuming and may produce inefficient cancellation decisions.
Operations Controllers GUI AADC
Method for Cancellation
Problem Statement
Airlines schedule aircraft through multiple steps to connect passengers and crews. Flight cancellation scenarios may impact downstream flights and connections at a great expense. Given that cancellation is unavoidable, which flights should be cancelled to reduce airline schedule disruption and passengers inconvenience?
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.
Scope
• Our focus is on two factors which lead to disruption :1) The affect a canceled flight could have on other
flights the same day
2) The reassignment of passengers on a canceled flight to other flights
• We are considering disruption caused to ONLY the current day's schedule
The Approach
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
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
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
Basis of our work
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
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
6:00 8:00 10:00 12:00 14:00 16:00
SDF
18:00 20:00 22:00
OAK
LAS
MCI
BNA
BWI
PHX
SAN
PIT
BDL
HOU
STL
SLC
OMA
BHM
PVD
MDW
N781
N430
N642WN
N730MA
N444 Space Time Diagram
TIME
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
First Step: Connectivity
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Solver
• Analysis and Conclusion
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
6:00 7:00
IND
BWI
8:00 9:00 10:00 11:00
ISP
N444
SDF
PVD
12:00
MDW
BDL
SAN
BNA
MCI
BHM
N730MA
N642WN
N430
N781
More Flights
No Flight
2 hr connection window (8:30-10:30)
TIME
START END
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
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.
BWI
PHX
IND
SAT
1
31
15
7
3
1
2
3
47
Flight Connectivity (CF) Factors
N444 N781
N642WN
N730MA
4
3
2
1
BWI
PHX
IND
SAT
1
31
15
7
3
1
2
3
47
Flight Connectivity (CF) Factors
N444 N781
N642WN
N730MA
5
3
2
1
4
3
2
1
BWI
PHX
IND
SAT
1
31
15
7
3
1
2
3
47
Flight Connectivity (CF) Factors
N444 N781
N642WN
N730MA
6 5
3
2
1
4
3
2
1
BWI
PHX
IND
SAT
1
31
15
7
3
1
2
3
47
Flight Connectivity (CF) Factors
N444 N781
N642WN
N730MA
6 5
3
2
1
4
3
2
17
100% flight connectivity [45min,120min]
Top 3 flights are connected to 55% of the flights throughout the day. All 3 flights leave close to 6:30 and are headed to MDW
A Flight arriving at small airport, ORF at 8:40 has low connectivity
Flights destined for airports with less traffic have low connectivity
Total flights during this day is 1853
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Scheduled Arrival Time
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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
y = 1.0224x
R2 = 0.998
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45 min to 180 min window
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Varying Connection windows180 min max vs. 150 min max Connection window: 240 min max vs.
120 min max
210 min max vs. 180 min max
y = 1.1795x
R2 = 0.9772
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45 min to 120 min window
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• 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)
Partial Connectivity
Airport Percent Connect
Year of 2002 Data
Author divides airports to :1. Major connecting airports
2. Partial Connecting airports
3. Non-connecting airports
Airports % connect
HOU 29.0%
MDW 23.5%
.…. …..
.…. …..
JAX 12.4%
AUS 10.7%
.…. …..
.…. …..
ALB 0.4%
BDL 0.0%http://www.erau.edu/research/BA590/chapters/ch1.htm
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
Comparing Graphs from the two methods
100 % Flight Connectivity APC Flight Connectivity
Low CF for early flight
Comparing results from the two methods
Tail number Leg Num origin1 dest1Scheduled out time
Schedule in time
cf_45_180100%
cf_45_180APC
N683 2 RNO LAS 8:00 9:10 527 462
N632 2 RNO PDX 8:05 9:25 292 118N617 2 RNO SEA 8:30 10:15 250 127
N687 3 RNO LAX 9:10 10:35 378 228
N649 1 RNO SLC 10:05 12:25 238 182
N651 3 RNO LAS 10:15 11:25 312 280
Table 2: Least disruptive (considering only connectedness) flight based on 100% Connectivity and Airport Percent Connect
Algorithm on other airlinesAirline B
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Airline A
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Airline C Three different airlines with 100% connectivity within a 45 to 180 minute time window
Second Factor• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
Passenger Factor
• Takes into consideration number of passengers on flight as well as remaining seats that day
• Equation:
• Higher penalty for a higher ratio
Seats Available ofNumber Total
Flighton Passengers ofNumber
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
Putting It All Together• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
Calculation of Disruption Index
• Disruption Index
• = W1(ConnFact) + W2 (α)(PaxFact)
W1 and W2 = Weights given to each factor
(a one time setting for each airline)α = Scaling factor for passengers
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
How it All Works
Functionality Test
• Algorithm tested for functionality using historical data
• Different airlines tested, each with different schedule date
• Shows how airline would use this data
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
Solving Tool
• Problem
• Solution
• Data
• Connectivity Factors
• Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusions
Solving Tool
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
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
References• http://www.isr.umd.edu/airworkshop/ppt_files/Ater.pdf
• Images:
• http://fly.faa.gov/Products/AADC/aadc.html
• http://ocw.mit.edu/NR/rdonlyres/Civil-and-Environmental-Engineering/1-206JAirline-Schedule-PlanningSpring2003/582393E6-2CA6-4CC1-AE66-1DAF34A723EA/0/lec11_aop1.pdf
Embry-Riddle Aeronautical University
• http://www.erau.edu/research/BA590/chapters/ch1.htm
Backup-Varying Connection windowsConnection Window: 45 to 150 Minutes
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Connection Window: 45 to 180 Minutes
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Connection Window: 45 to 210 Minutes
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Connection Window: 45 to 240 Minutes
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Connection window: 45 to 150min Connection window: 45 to 180min
Connection window: 45 to 210min Connection window: 45 to 240min
Investigating Connectedness-Sensitivity
Origin Destination Departure Arrival Destination Size1 CF12 CF23
BWI BUF 09:55 11:00 23 1 60
PHX ELP 08:15 10:35 19 1 78
PHX ELP 10:55 13:00 19 1 80
MDW DTW 10:40 12:45 25 1 95
MDW OMA 09:45 11:05 28 1 117
TPA MSY 08:50 09:25 34 1 157
BWI RDU 07:15 08:20 38 1 175
BNA CLE 07:30 09:55 36 1 176
MDW IND 06:45 07:40 24 1 185
TPA JAX 07:15 08:05 17 1 251
1. In this case size refers to the total number of entering and departing flights from the airport2. CF1 is the connectivity factor for a 45 to 150 minute connection window.3. CF2 is the connectivity factor for a 45 to 180 minute connection window
The highest 10 increases in CF by percent based upon adding 30 minutes to the connection window:
Airport Percent Connect CFs
Connection Window: 45 to 180 MinutesAccounting for Passenger Connections
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Low CF for early flight
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
…
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
Percent Connectivity Airline B
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Connectivity Factors, 100% Connectivity Connectivity Factors, Percent Passenger Connectivity
As before, accounting for percent connectivity had a significant effect on the outputs. A similar decrease indata occurred for Airline C