1. SimulationModeling Project | ManishaArora
Simulation Modeling Project
ManishaArora, M10666244
Project: Simulation of flight traffic at New Delhi, India airport

2. SimulationModeling Project | ManishaArora
Index
Objective…………………………………………………………………………………………………………………………………………….….3
Introduction …………………………………………………………………………………………………………………………………………..3
Assumptions ……………………………………………………………………………………………………………………………………..…..3
Data Input……………………………………………………………………………………………………………………………………….…….3
SimulationModel Set-up………………………………………………………………………………………………………………..……..5
Statistical Analysis …………………………………………………………………………………………………………………………..…..12
6.1 Mean ComparisonthroughOutputAnalyzer ……………………………………………………….……..12
6.2 ScenarioComparison throughProcessAnalyzer………………………………………………..……....13
ConclusionandRecommendation …………………………………………………………………………………………….………….15
References…………………………………………………………………………………………………………………………………………….15

3. SimulationModeling Project | ManishaArora
1. Objective:
The objective istosimulate the flighttraffic,bothlandingandtake-off,atthe New Delhi,Indiaairport,
checkfor scope of improvementandoptimizethe waitingtimesandqueue lengthsof flights,if possible.
Further,the needforopeninganewrunwayand itsimpacton the simulationoutputssuchaswaittimes
and queue lengthswill be assessed.
2. Introduction:
IndiraGandhi International Airportisthe primaryaviationhubforthe National Capital Regionof Delhi,
India.Itis the busiestairportinthe countryin termsof passengertraffic,handlingover40 million
passengers.The airporthas2 mainrunwayswhichare alwaysinoperation.There isalsoanauxiliary
runwaythat opensonlyduringpeakloads. Boththe runwayscan be usedforlandingsandtake-offs.
However,forthe scope of thisproject,we will consideronly2main runwayswhichwill distributethe
loadequallybetweenthemselves.
The airport witnessedapproximately314,000 flightmovementslastyear. The purpose of thissimulation
isto lookat the currentqueuesand waitingtimes,andexamine the effectof anadditional runwayon
flightqueuesandwaitingtimes.
3. Assumptions:
a) All flightsfollowthe specifieddistributionsforall daysof the year.Anyseasonal variations
have not beentakenintoconsideration.
b) The ratio of International toDomestictoCargo flightshave beenfixedto3:6:1, for both
arrivalsanddepartures.
c) All flights,irrespectiveof theirtype,have beenassumedtouse the runwayfora fixedlength
of time.
d) There isa constantdelayof 10minutes betweentwoconsecutiveflightsusingthe same
resource.
e) Anyof the specifiedparametersorprocessesdonotvarywithanyexternal factorssuchas
weatherconditionsorprocessefficiency.
4. Data Input:
Data about passengertrafficandrunwayshasbeencollectedfrom www.wikipedia.com.Further,data
aboutflightmovements,arrival anddeparture timeshasbeencollectedfromthe airport’sofficial
website www.newdelhiairport.com.
The data about flightmovementswasanalyzedandbucketedinto24sectionstoindicate numberof
arrivalsanddepartureseveryhour.Thisdata was fedintoArenathroughthe EntitySchedule,as
depictedbelow:

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Fig 1: Interarrivaltimes perhourfor Flight Arrival
Fig 2: Interarrivaltimes perhourfor Flight Departures

5. SimulationModeling Project | ManishaArora
5. SimulationModel Set-up:
Fig 3: Arena Model1 – with SeparateRunways
1. The above data was modelledinArena,using separate create modulesforArrival and
Departure.
2. The time for eachof the take-off andlandingprocesses foranairplane is3 minutes Further,we
observe aconstantdelayof 2 minutesafteraflightdeparture before the runwaycanbe usedby
anotherflight,due toairturbulence.Thisresultsinaqueue forrunwayusage at both arrival and
take-off processes.
3. Domesticflightsconstitute 60%of the total air traffic,followedby30% International flightsand
10% cargo flights.
4. International flights,both arrivinganddeparting,are giventhe highestpriority.Thisisfollowed
by Domesticflights,while Cargoflightsholdthe lowestpriority.
The above concepthas beensimulatedinArenaasfollows:
1. Create Module forArrival – Used fordefining specifyingflightarrival data
Fig 4: Arrivalcreate module

6. SimulationModeling Project | ManishaArora
2. Create Module forDeparture
Fig 5: Departurecreate module
3. DecisionModule
Fig 6: Decision module
4. AssignModules –Three assignmoduleshave beenplaced,tosetpriorityfordifferentflight
types
Fig 7: Assign modules

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5. Delay – Two delayblockswere placedassigning2-minute delayforeachof the landingandtake-
off process
Fig 8: Delay module
6. Process – Two separate processeshave beencreatedforlandingandtake off
Fig 9: Decision module
7. Dispose – a module todispose-off entitiesatthe endof the process
Fig 10: Disposemodule
Two differentresourceshave beenspecifiedinthe system, Runway1forarrivingflightsandRunway2
for departingflights.

8. SimulationModeling Project | ManishaArora
Fig 11: ResourceAllocation for Model1
Two separate queueshave beensetupinthe system – Landingand take-off queue.Eachof the queue
usesa differentresource.The Landingqueue usesresource Runway1,while the Take-off queue uses
queue Runway2.The queuesare processedinthe orderof theirpriorityi.e.International flightsare
processedfirst,followedbyDomestic,followedbyCargo.
Fig 12: Queuefor Model1
The systemhas beensetupto perform20 identical replications,foreachof the 24-hour period.Base
unitshave beenconsideredas‘Minutes’.
Fig 13: Run Setup for Model1

9. SimulationModeling Project | ManishaArora
Some of the keyOutputparametersobservedwere NumberOut, Waittime,Queue Length,Total Time
inSystem, andResource Utilization.
Fig 14: Simulation OutputReportforModel1, showing entitiesoutof the system
We observe the total numberof entitiesprocessedare 370, excludingthose inservice orqueue atthe
endof the simulation.
Fig 15: Simulation OutputReportforModel1, showing entity waitand totaltimes
The average waittime isalmost1 minute,whichisnotsignificant.However,whenwe lookatthe
maximumvalue of ~29minutesasthe waittime for a flight,we conclude thatitishigh.
Further,the maximumtotal time spentinsystemis~34 minutes,whichisalsosignificant.
Fig 16: Simulation OutputReportforModel1, showing queuewaittimeand queuelength

10. SimulationModeling Project | ManishaArora
The maximumwaitingtime forLanding andTake-off queuesis~29 minutesand~26 minutes
respectively.The queuelengthmaygoas highas 6 i.e.the maximumnumberof flightswaitinginthe
queue atany time is6 eachin landingandtake-off queues.
Fig 17: Simulation OutputReport forModel1, showing averageresourceutilization
We observe thatthe utilizationof the 2 runwaysisapproximately38%.
The highwaitingtimesandqueue lengths,coupledwithlow utilizationsof the resource indicate scope
for improvementinthe currentmodel. Toovercome this,we create anotherArenamodel,inwhichthe
runwaysforboth Landingandtake-off have beencombined.Hence,the plane canuse anyof the two
runways,whicheverisfree.
Fig 18: Arena Model2 – with Combined Runways
In the Arenamodel above,the separate landingandtake-offprocesseshave beencombinedintoa
single process –Runway Usage,where the resource Runwayisbeingused.The capacityof thisresource
has beenspecifiedastwo.
Fig 19: ResourceAllocation for Model2

11. SimulationModeling Project | ManishaArora
The queueshave beencombinedintoone calledthe RunwayUsage Queue,utilizinganyof the two
runways.Note thatthe usage of the resource isstill basedonPriorityi.e.International flights,followed
by Domesticflights,followedbyCargo.
Fig 20: Queuefor Model2
The Outputparameters – NumberOut,waittime,Queue Length,Total Time inSystem, andResource
Utilizationwere observedagain:
Fig 21: Simulation OutputReportforModel2, showing entitiesoutof the system
We observe the total numberof entitiesprocessedremainas370, excludingthose inservice orqueue at
the endof the simulation,whichisequal tothose processedinModel 1.
Fig 22: Simulation OutputReportforModel2, showing entity waitand total times
The average waittime has reducedfrom~1 minute to0.3 minutes.Also,the maximumwaittime has
reducedfrom~29minutesto ~16 minutes,whichisasignificantreduction.
Further,the maximumtotal time spentinsystemhasreducedfrom~34 minutes to~21 minutes.This
suggestsa significantimprovementinoutputstatisticsfrommodel1to model 2.

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Fig 23: Simulation OutputReportforModel2, showing queuewaittimeand queuelength
The queue lengthhasbeendecreasedfrom6eachfor landingandtake-off (thoughtheymightnotreach
theirmaximumvalue atthe same time) to6 onlyfor the runway.
Fig 24: Simulation OutputReportforModel2, showing averageresourceutilization
The average scheduledutilizationforeachrunwayhasremained the same to~38%.
6. Statistical Analysis:
6.1 Mean Comparison through Output Analyzer
We use the OutputAnalyzertodeterminethe change statistically.A pairedt-testwitha95% interval to
testthe statistical significance of the changesinwaittimesandqueue lengths.
Fig 25: Wait Time Comparison between model1and 2 using OutputAnalyzer

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Fig 26: QueueLengthComparison between model1 and 2 using OutputAnalyzer
We observe the followingforboththe outputparametersfromthe above analysis:
 meanzerodoesnot lie inthe 95% confidence interval.Thissuggeststhatthere isastatistically
significantdifferencebetweenthe twomodels
 Null hypothesisstatesthatthe differencebetweenthe meansiszero.However,we rejectthe
null hypothesisat0.05 significance level
 A positive difference forbothqueue lengthandwaittime states thatthere isa decrease in each
of the parameters frommodel 1to model 2, whichisdesired.
6.2 Scenario Comparison through Process Analyzer
We analyze the twoscenarios (Separate andCombinedrunways) throughthe ProcessAnalyzerto
compare the response variablesthroughvariationinControl variables.Further,eachscenariois
duplicatedtoanalyze twonewscenarioswithvariedresources.
Fig 27: Analysisof responsevariablesacrossmodelsthrough changein controlvariables
Followingare the detailsof the variousscenariossimulatedabove:
 Model 1 – Base scenariowithtwoparallel runways,one eachforlandingandtake-off
 Model 2 – Simulatedscenariowithanincreasedresource forLanding
 Model 3 – Simulatedscenariowithanincreasedresource fortake-off
 Model 4 – Base scenariowithtwocombinedrunways,bothof whichcanbe usedforeither
landingortake-off

14. SimulationModeling Project | ManishaArora
 Model 5 – Simulatedscenariowiththree combinedrunways,all of whichcanbe usedforeither
landingortake-off
From the above figure,we observethe following:
 Model 1 has the highestflightwaitingtime.Thisisexpectedbecausethere are twoseparate
queuesforlandingandtake-off flights.Thistime however,decreaseswhenanadditional
resource isopenedforeitherof the processes,ascanbe observedinmodels2and 3.
 Model 4 whichhascombinedresourcesforboththe processes, hasalowerflightwaittime than
models1,2 and 3, suggesting animprovedmodel
 Model 5 isa simulatedmodel of model 4above,withanincreasedresource capacity.This
increase resultsinreductionof flightwaittime significantly.
 Also,the landingand take-off queueshave avalue of ~3.5 each inmodel 1. Hence,at anypoint
of time,there canbe more than 4 planeswaitingintotal foreitherlandingortake-off.Thiscan
alsobe observedfromthe OutputReportsabove whichindicate amaximumvalue of 6at any
instant.
 Thisqueue lengthisdecreasedfrommodel1to model 2 and 3, whenan additional resourceis
added,thoughthe queuesforarrivinganddepartingflightsremainseparateanduse separate
resources.
 For the combinedmodel,the resourcesforboththe processeshave beencombinedandthe
queue lengthisdecreased to4. Thisis because the flightsnow donothave to waitintheir
respective queue nowbutcanuse any of the twoavailable resources.
 Model 5 observesareducedqueue lengthof ~2.7 flightsdue toadditionof a resource tomodel
4. This isbecause ithas 3 runways,all of whichcan be usedforeitherarrivingordeparting
planes.Hence,thismodel emergesasthe bestscenariopossibledue toreductioninboth
waitingtime andqueue length.

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Fig 28: Graph of responsevariablethrough changein control variables,depicting bestscenario
We have plottedabox-and-whiskerplotforeachof the scenarios,depictingthe parameters. Fromthe
graph above,Model 5 (Combinedrunwayswithincreasedcapacity) isthe bestscenario,followedby
Model 4(Combinedrunways).Thisisbasedconsideringthatsmallervaluesof waittime andqueue
lengthare better.Also,the tolerance level hasbeenconsideredaszero.
7. Conclusionand Recommendation:
The above analysisshowsthatwe needtocombine the tworunwaysintoa single resource tooptimize
the queue lengthandwaittime forthe flights.Combiningthe tworesourcesintoasingle resource with
capacityas 2 reducesthe maximumwaittime from~30minutesto~16 minutes.The average waittime is
alsoreducedfrom~1 minute to~0.3 minutes.
Further,if there are no cost constraints,we canthinkof increasingthe runwaycapacitytoreduce the
maximumqueue lengthduringpeakloads. Thiswouldalsohelpinaccommodatinganyincreasedload
duringthe peakfestive season.
8. References
a) SimulationwithArena(W.David.Kelton,Randall.P.Sadowski,Nancy.B.Zupick,Rockwell
Automation)
b) www.newdelhiairport.com
c) www.wikipedia.com
d) MilanJanic,Air TransportSystemAnalysisandModelling(Google Books)