Transcript Optimization of Airline Flight Cancellation Decisions.

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
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
Flight
Schedule
generation
Fleet
assignment
Aircraft
maintenance
routing
Crew
Scheduling
Yield
Management
OPERATIONS
MANAGEMENT
Background
 According to Bureau of Transportation Statistics (BTS)
American Airline (14.8%)*
% Cancelled
SouthWest (12.2%)*
% Cancelled
United (11.5%)*
% Cancelled
Delta (10.8%)*
% Cancelled
2003
2004
2005
2006
2007
2008 Average
SD
1.61
1.78
1.45
1.57
2.83
2.70
1.99
0.61
1.01
1.02
0.85
0.81
0.85
0.80
0.89
0.10
1.09
1.18
1.30
2.05
2.43
2.62
1.78
0.67
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
258 Domestic Flights Cancelled Per Day
Average
Stdev
1.57 %
0.65 %
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
Request scheduled departing flights
Show 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
AADC
Airline
Operations GUI
Flight Cancellation Decision Tool
Method for Cancellation
• 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
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
N444
Space Time Diagram
SDF
OAK
LAS
N781
MCI
BNA
N430
PHX
BWI
N730MA
PIT
SAN
BDL
N642WN
HOU
STL
MDW
PVD
BHM
OMA
SLC
6:00
8:00
10:00
12:00
TIME
14:00
16:00
18:00
20:00
22:00
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
IND
SDF
N444
2 hr connection
window (8:30-10:30)
N642WN
More Flights
No Flight
N781
BNA
BWI
PVD
MCI
START
END
MDW
N730MA
BDL
N430
BHM
SAN
ISP
6:00
7:00
8:00
9:00
TIME 10:00
11:00
12:00
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.
Flight Connectivity (CF) Factors
N444
BWI
7
4
N781
1
5
N642WN
PHX
3
1
3
3
1
7
IND
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
N781
1
5
N642WN
PHX
3
1
3
3
1
7
IND
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
1
5
N781
1
N642WN
PHX
3
1
3
3
1
7
IND
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
1
5
N781
1
N642WN
PHX
3
1
3
3
1
7
IND
2
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
1
5
N781
1
N642WN
PHX
3
1
3
3
3
1
7
IND
2
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
1
5
N781
1
N642WN
PHX
3
4
1
3
3
3
1
7
IND
2
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
1 5
5
N781
1
N642WN
PHX
3
4
1
3
3
3
1
7
IND
2
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
4
6
1 5
5
N781
1
N642WN
PHX
3
4
1
3
3
3
1
7
IND
2
2
SAT
1
N730MA
Flight Connectivity (CF) Factors
N444
BWI
7
7
4
6
1 5
5
N781
1
N642WN
PHX
3
4
1
3
3
3
1
7
IND
2
2
SAT
1
N730MA
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
Total flights
during this
day is 1853
1100
1000
Connectivity Factor
900
800
700
A Flight arriving at small
airport, ORF at 8:40 has low
connectivity
600
500
400
300
Flights destined for
airports with less
traffic have low
connectivity
200
100
0
0:00
3:00
6:00
9:00
12:00 15:00 18:00 21:00
Scheduled Arrival Time
0:00
3:00
6:00
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
Varying Connection windows
Varying Connection windows
Connection window: 240 min max vs.
120 min max
180 min max vs. 150 min max
1200
1200
1000
45 min to 240 min window
y = 1.0453x
R² = 0.9966
45 min to 180 min window
800
600
400
200
800
y = 1.1795x
R2 = 0.9772
600
400
200
0
0
0
200
400
600
800
1000
1200
1200
y = 1.0224x
R2 = 0.998
1000
800
600
400
200
0
0
200
400
600
800
45 min to 180 min window
1000
0
200
400
600
800
45 min to 120 min window
min180
to 150min
min window
210 min max45vs.
max
45 min to 210 min window
1000
1200
1000
1200
Partial Connectivity
• 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)
Airport Percent Connect
Year of 2002 Data
Author divides airports to :
1. Major connecting airports
2. Partial Connecting airports
3. Non-connecting airports
http://www.erau.edu/research/BA590/chapters/ch1.htm
Airports
% connect
HOU
29.0%
MDW
.….
23.5%
…..
.….
…..
JAX
12.4%
AUS
.….
10.7%
…..
.….
…..
ALB
0.4%
BDL
0.0%
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
Low CF for
early flight
100 % Flight Connectivity
APC Flight Connectivity
Comparing APC and 100% Connectivity
Comparing results from the two methods
Tail number Leg Num
origin1
dest1
Scheduled Schedule in cf_45_180 cf_45_180
out time
time
100%
APC
N683
2
RNO
LAS
8:00
9:10
527
462
N632
2
RNO
PDX
8:05
9:25
292
118
N617
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 airlines
Airline B
1200
1100
1100
1000
1000
900
900
Connectivity Factor
1200
800
700
600
500
400
800
700
600
500
400
300
300
200
200
100
100
0
0:00
3:00
6:00
9:00
12:00 15:00 18:00 21:00
0:00
3:00
0
0:00
6:00
Airline C
6:00
9:00
12:00
15:00
18:00
21:00
0:00
Three different airlines with 100%
connectivity within a 45 to 180
minute time window
1200
1100
1000
900
800
700
600
500
400
300
200
100
0
0:00
3:00
Arrival Time
Arrival Time
Connectivity Factor
Connectivity Factor
Airline A
3:00
6:00
9:00
12:00
15:00
Arrival Time
18:00
21:00
0:00
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:
Number of Passengers on Flight
Total Number of Available Seats
• Higher penalty for a higher ratio
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
Spreadsheet Solver
How it All Works
•
•
•
•
•
Problem
Solution
Data
Connectivity Factors
Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
Functionality Test
• Algorithm tested for functionality using
historical data
• Different airlines tested, each with different
schedule date
• Shows how airline would use this data
PF
Tail #
Departure
Origin Destination
Time
Arrival
Time
DI
Weighted
CF
Weigh
ted
PF
N343NB
MSP
SLC
9:11
11:04
1
0.5
2.4
0.4
N301US
MSP
MCO
10:21
14:19
1
0.5
2.6
0.5
N313US
MSP
SMF
9:16
11:07
1
0.5
2.8
0.5
N596NW
MSP
PDX
9:30
11:20
2
1.5
1.5
0.3
N362NB
MSP
IAH
9:10
11:53
2
1.5
1.9
0.3
N348NB
MSP
EWR
10:47
14:17
2
1.5
2.1
0.4
N327NW
MSP
SJC
10:20
12:23
2
0.5
7.9
1.4
N375NC
•
•
MSP
•
•
RSW
•
•
10:18
•
•
14:28
•
•
2
•
•
1.5
•
•
3.6
•
•
0.6
•
•
N378NW
MSP
TPA
10:23
14:12
6
5.0
4.1
0.7
N777NC
MSP
MEM
10:14
12:07
6
6.0
1.0
0.2
N8925E
MSP
MKE
10:08
11:08
6
6.0
1.2
0.2
N780NC
MSP
DTW
10:06
12:52
8
7.5
0.5
0.1
24.5
21.1
N338NW
MSP
PSP
9:20
11:01
26
2.0
135.
1
N303US
MSP
MIA
10:30
14:48
35
14.0
116.5
Destinati Departure
on
Time
Arrival
Time
DI
Weighted CF
PF
Weighted PF
Tail #
Origin
N171US
CLT
SFO
9:16
12:03
12
1.5
3.1
10.8
N514AU
CLT
ORF
9:26
10:29
13
3.5
2.8
9.7
N449US
CLT
BUF
9:19
10:47
17
12.0
1.5
5.2
N525AU
N439US
CLT
CLT
SRQ
MIA
9:24
9:54
11:12
12:01
23
24
2.0
20.5
6.0
1.1
20.5
3.7
N749US
N453UW
N426US
CLT
CLT
CLT
DEN
BWI
JAX
9:38
8:03
9:45
11:31
9:22
10:59
27
29
32
23.5
26.5
28.5
1.1
0.8
1.0
3.8
2.8
3.4
N530AU
CLT
DFW
9:28
11:20
35
28.5
1.9
6.4
N459UW
•
•
N918UW
CLT
•
•
CLT
PBI
•
•
LAS
9:37
•
•
9:47
11:43
•
•
11:24
35
•
•
71
28.5
•
•
66.5
1.9
•
•
1.4
6.6
•
•
4.9
N533AU
CLT
DFW
8:09
9:55
72
66.5
1.5
5.1
N939UW
N922UW
CLT
CLT
MCO
TPA
9:58
9:52
11:36
11:37
79
79
77.0
75.0
0.6
1.2
2.1
4.2
N457UW
CLT
PHL
9:30
10:58
82
79.0
0.8
2.9
N721UW
CLT
BOS
8:08
10:14
94
91.0
0.8
2.9
N574US
CLT
CMH
9:59
11:16
189
2.0
54.2
187.2
Solving Tool
•
•
•
•
•
Problem
Solution
Data
Connectivity Factors
Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusion
Tom’s Solver hyperlink
Solving Tool
•
•
•
•
•
Problem
Solution
Data
Connectivity Factors
Passenger Factors
• Disruption Index
• Analysis
• Solver
• Conclusions
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/1206JAirline-Schedule-PlanningSpring2003/582393E6-2CA6-4CC1-AE661DAF34A723EA/0/lec11_aop1.pdf
Embry-Riddle Aeronautical University
• http://www.erau.edu/research/BA590/chapters/ch1.htm
Question
Backup-Varying Connection windows
Connection Window: 45 to 180 Minutes
Connection
window: 45 to 180min
1100
1200
1000
1100
900
1000
800
900
Connectivity Factor
Connectivity Factor
Connection Window: 45 to 150 Minutes
Connection
window: 45 to 150min
700
600
500
400
300
200
100
0
0:00
800
700
600
500
400
300
200
100
3:00
6:00
9:00
12:00 15:00 18:00 21:00
0:00
3:00
0
0:00
6:00
3:00
6:00
9:00
Time
3:00
6:00
Connection Window: 45 to 240 Minutes
Connection
window: 45 to 240min
1200
1200
1100
1100
1000
1000
900
Connectivity Factor
900
Connectivity Factor
0:00
Time
Connection Window: 45 to 210 Minutes
Connection
window: 45 to 210min
800
700
600
500
400
300
200
800
700
600
500
400
300
200
100
100
0
0:00
12:00 15:00 18:00 21:00
3:00
6:00
9:00
12:00 15:00 18:00 21:00
Time
0:00
3:00
6:00
0
0:00
3:00
6:00
9:00
12:00 15:00 18:00 21:00
Time
0:00
3:00
6:00
Investigating Connectedness-Sensitivity
The highest 10 increases in CF by percent based upon adding 30 minutes to the
connection window:
1.
2.
3.
Origin
Destination
Departure
Arrival
Destination Size1
BWI
BUF
09:55
11:00
PHX
ELP
08:15
PHX
ELP
MDW
CF12
CF23
23
1
60
10:35
19
1
78
10:55
13:00
19
1
80
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
In this case size refers to the total number of entering and departing flights from the airport
CF1 is the connectivity factor for a 45 to 150 minute connection window.
CF2 is the connectivity factor for a 45 to 180 minute connection window
Airport Percent Connect CFs
Connection Window: 45 to 180 Minutes
Accounting for Passenger Connections
1100
1000
Low CF for early flight
Connectivity Factor
900
800
700
600
500
400
300
200
100
0
0:00
3:00
6:00
9:00
12:00
15:00
Time
18:00
21:00
0:00
3:00
6:00
• EVM
• WBS
• GANNT
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
Connectivity Factors, 100% Connectivity
1000
900
900
800
800
Connectivity Factor
Connectivity Factor
1000
700
600
500
400
300
700
600
500
400
300
200
200
100
100
0
0:00
3:00
6:00
9:00
12:00
15:00
Arrival Time
18:00
21:00
0:00
Connectivity Factors, Percent Passenger Connectivity
0
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
Arrival Time
As before, accounting for percent connectivity had a significant effect on the outputs. A similar decrease in
data occurred for Airline C
Agents/Stakeholders
• Airline Operations Control
• FAA
• Air traffic controllers
• Passengers
• Pilots/flight crew
• Maintenance crew