Studies in Route Optimization of Cargo Airlines in India

Dr. Rajkumar S. PantAssociate Professor of Aerospace Engineering

Indian Institute of Technology, Bombayrkpant@aero.iitb.ac.in

Airports

Routes

Aircraft

Scheduled Flights

A

B

CD

Typical Airline Network

Airports

Aircraft

Routes

Schedule

Time varying Demand

Literature Review Objectives – Kanafani (1982),Teodorovic (1988)

Max. RevenueMin. CostMax. ProfitMax. Level of ServiceMax. Aircraft Utilization

Literature Review

Objectives – Kanafani (1982),Teodorovic (1988) Multi Criteria Model – Teodorovic & Krcmar-nozic (1989)

Max. ProfitMax. number of passenger flownMin. Schedule Delay

Literature Review

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

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

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

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

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

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

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

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 Arrive at ‘‘Schedule-of-the-day“

Maintenance and operational constraints applicable

Combined scheduling and optimisation Route selection using Grey Theory (Deng, 1982)

Optimization of user-selectable objective functions

Airline can assign priorities to certain routes

Inputs required

Airport Details

Network Details

Demand Data

Base Station Details

Fleet Details

Route Priorities (if any)

Overview of the methodology

Control Parameters Demand index Cost Index Time Index Route Priority Index

Schedule Generator

Objective FunctionsMax. 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

Airport Slots

Break Even Load Factor

Base Station and Hanger Capacity

Maintenance

Case Study for Overnight Express Cargo Airline

Overnight Express Cargo

Late night cutoffs, early morning

delivery

Varying demand

Dedicated Freighter aircraft

Fixed window for Flight Operations

Assumptions

Dedicated Cargo airline

Demand is known a priori

Route Lengths ≤ Harmonic Range

Same Turn Around Time at all airports

Constraints in Schedule Generation

Operational Airport Slot availability

Break-even Load Factor

Operating time window

Maintenance Base station to go to at the end of the day

Hangar Capacity

Maximum flight time available for each aircraft

Typical Results

18%

-12%

33%

8%

20%

-20%

-10%

0%

10%

20%

30%

40%

Cargo Cost Time Quality ofService

Cargo/Cost

Improvements compared to existing schedule being operated

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/CostMax Cargo/Time 1.278 1.016 1.297 0.978 0.985 1.258

Conclusions

Methodology for demand responsive scheduling of day’s operation Grey Theory for route selection Genetic Algorithms for Optimization

Case Study for Express Cargo airline ~ 20% improvement

Cargo Carried Cargo/Cost

Thank you

By Deng (1982)

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

rsc

- Can handle systems for which exact information is lacking

- Can deal with multidisciplinary characteristics of the system

Grey Theory Grey Theory

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ccn

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xx x

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x xx x x

x x

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cncp

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xx x

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Whitening Functions

Greater then a numberLess then a number Approx to a number

3 Types