Studies in Route Optimization of
Cargo Airlines in India
Dr. Rajkumar S. Pant
Associate Professor of Aerospace Engineering
Indian Institute of Technology, Bombay
rkpant@aero.iitb.ac.in
Typical Airline Network
Routes
A
Airports
Airports
B
Aircraft
Aircraft
Routes
Schedule
Time
varying
Demand
Scheduled Flights
D
C
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Max. Revenue
Min. Cost
Max. Profit
Max. Level of Service
Max. Aircraft Utilization
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Max. Profit
Max. number of passenger flown
Min. Schedule Delay
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Min. Canceled flights and Min. Total Passenger Delay
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Route Selection – Hsu and Wen (2000)
Application of Grey Theory
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Route Selection – Hsu and Wen (2000)
Crew –Kornilakis et al (2002)
Crew pairing & Assignment
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Route Selection – Hsu and Wen (2000)
Crew –Kornilakis et al (2002)
Maintenance- Sriram and Haghani (2003)
Minimum Maintenance Cost
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Route Selection – Hsu and Wen (2000)
Crew –Kornilakis et al (2002)
Maintenance- Sriram and Haghani (2003)
Departure Time: Chang & Schonfeld (2004), Pollack (1974)
Min. average schedule delay per passenger
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Route Selection – Hsu and Wen (2000)
Crew –Kornilakis et al (2002)
Maintenance- Sriram and Haghani (2003)
Departure Time: Chang & Schonfeld (2004), Pollack (1974)
Air Cargo fleet routing: Yan, Chen & Chen (2006)
Dedicated methodology for Cargo Airlines
Literature Review
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Objectives – Kanafani (1982),Teodorovic (1988)
Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)
Day of Operation – Teodorovic and Stojkovic (1995)
Fleet Assignment –Gvozdenovic (1999)
Route Selection – Hsu and Wen (2000)
Crew –Kornilakis et al (2002)
Maintenance- Sriram and Haghani (2003)
Departure Time: Chang & Schonfeld (2004), Pollack (1974)
Air Cargo fleet routing: Yan, Chen & Chen (2006)
Integrated Transportation Network Design & Optimization- Taylor &
De-Weck (2007)
Optimization of Aircraft & Route Network at one go
Methodology for Airline Network
Scheduling and Optimization
Features

Demand responsive, flexible scheduling
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Arrive at ‘‘Schedule-of-the-day“
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Maintenance and operational constraints applicable
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Combined scheduling and optimisation
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Route selection using Grey Theory (Deng, 1982)
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Optimization of user-selectable objective functions
Airline can assign priorities to certain routes
Inputs required
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Airport Details
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Network Details
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Demand Data
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Base Station Details
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Fleet Details
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Route Priorities (if any)
Overview of the methodology
Control Parameters
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Demand index
Cost Index
Time Index
Route Priority Index
Schedule Generator
Objective Functions
Max. Cargo
Total Cargo carried over all the routes
Min. Cost
Total Operating Cost over all the routes
Min. Time
Total flight time of all aircraft on all routes
Min. QOS
Variance
Difference between required and allotted frequency on
all OD pairs
Max. Cargo/Cost Ratio of total amount of Cargo carried over the network
with the Total Operating Cost incurred
Max. Cargo/time Ratio of total amount of Cargo carried over the network
and summation of the total flight time of all aircraft on
all routes
Constraints
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Airport Slots
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Break Even Load Factor
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Base Station and Hanger Capacity

Maintenance
Case Study for Overnight Express
Cargo Airline
Overnight Express Cargo

Late night cutoffs, early morning delivery
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Varying demand
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Dedicated Freighter aircraft
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Fixed window for Flight Operations
Assumptions

Dedicated Cargo airline
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Demand is known a priori
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Route Lengths ≤ Harmonic Range
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Same Turn Around Time at all airports
Constraints in Schedule Generation

Operational
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Airport Slot availability
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Break-even Load Factor
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Operating time window
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Maintenance
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Base station to go to at the end of the day
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Hangar Capacity
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Maximum flight time available for each aircraft
Typical Results
Improvements compared to existing schedule being operated
40%
33%
30%
20%
20%
18%
8%
10%
0%
Cargo
Cost
-10%
-12%
-20%
Time
Quality of
Service
Cargo/Cost
Sample Output
Objective function Cargo Cost
Time
QOS Variance Cargo/Time Cargo/Cost
Max Cargo
1.218 1.117 1.422
2.339
0.856
1.090
Min Cost
0.924 0.885 1.167
2.134
0.792
1.043
Max Time
1.020 1.138 1.490
2.997
0.685
0.897
Min QOS Variance
1.231 1.034 1.339
0.898
0.920
1.191
Max Cargo/Cost
Max Cargo/Time
1.278 1.016 1.297
0.978
0.985
1.258
Conclusions
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Methodology for demand responsive
scheduling of day’s operation
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Grey Theory for route selection
Genetic Algorithms for Optimization
Case Study for Express Cargo airline
~ 20% improvement
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Cargo Carried
Cargo/Cost
Thank you
Grey Theory
By Deng (1982)
- Can deal with multidisciplinary characteristics of the system
- Can handle systems for which exact information is lacking
 rsc
Parameters
Definitions
Examples in Airline Network
Candidates
(C1,C2,C3..)
List of Possible solutions
Direct flight
Indirect flights
Properties/
Index
(P1,P2,P3..)
Figure of merits on which the
selection is based
Number of Intermediate Stops mrsc
Route Length Index
Traffic concentration
Categories
(Cat1,Cat2, Cat3… )
List of possible decisions to which
a candidate can belong
Select
Reject
Probable
Whitening
Functions
Instrument to take decision
Less than a number
Greater than a number
Approximate to a number
 rsc
Whitening Functions
3 Types

x
xc


f pn ( x)  
 ( xm  x)
 xm  xc
Less then a number

0  x  xc 


xc  x  xm 

x

0  x  xc 

n
Approx
Greater
xc
f p ( x)toanumber
 then a number
1

x

x
c

