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

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12:00

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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

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Flight Connectivity (CF) Factors

N444 N781

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Flight Connectivity (CF) Factors

N444 N781

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Flight Connectivity (CF) Factors

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Flight Connectivity (CF) Factors

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Flight Connectivity (CF) Factors

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Flight Connectivity (CF) Factors

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Flight Connectivity (CF) Factors

N444 N781

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Flight Connectivity (CF) Factors

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Flight Connectivity (CF) Factors

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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

Varying Connection windows

y = 1.0224x

R2 = 0.998

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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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• 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 APC and 100% Connectivity

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

Spreadsheet Solver

• 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

Tom’s Solver hyperlink

• 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

Question

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

• 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

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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

Agents/Stakeholders

• Airline Operations Control

• FAA• Air traffic controllers

• Passengers

• Pilots/flight crew

• Maintenance crew