airport baggage handling systems by vitalis okafor & maxwell ble

8/11/2019 Airport Baggage Handling Systems by Vitalis Okafor & Maxwell Ble

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AIRPORT BAGGAGE HANDLING SYSTEMS:

A Discrete-Event Simulation Study

y Vitalis Okafor and Maxwell l


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ContentsFigures & Tables ........................................................................................................................................ 2

Model Construction ...................................................................................................................................... 3

Data Input ..................................................................................................................................................... 6

Exponential distribution ........................................................................................................................ 6

Gamma distribution .............................................................................................................................. 6

Loglogistic distribution .......................................................................................................................... 6

Uniform distribution ............................................................................................................................. 7

Weibull distribution .............................................................................................................................. 7

Model Verification & Validation ................................................................................................................... 8

Results ........................................................................................................................................................... 9

Case 1 ........................................................................................................................................................ 9

Case 2 ...................................................................................................................................................... 10

Conclusions ................................................................................................................................................. 12

Works Cited ................................................................................................................................................. 13

Appendices .................................................................................................................................................. 14

Appendix 1: Relevant Formulas from ExpertFit Manual [4] ................................................................... 14

Appendix 2: Java code used to compute the formulae of the different distributions ........................... 16

Figures & Tables

Figure 1: Conceptual Model of Airport Baggage Handling System ............................................................... 4

Figure 2: Specification Model of Airport Baggage Handling System ............................................................ 5

Figure 3: Processes Average Total Baggage Seized..................................................................................... 11

Figure 4: Processes Scheduled Utilization .................................................................................................. 11

Table 1: Results for Case 1 ............................................................................................................................ 9

Table 2: Results for Case 2 .......................................................................................................................... 10
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Model Construction

The baggage handling system simulated in this model was constructed on Arena Simulation

Software Student Edition mostly using information from the baggage handling systems at Denver

International Airport [1] and Rockford Internal Airport [2]. The model begins with passengers

arriving at the check-in counter where their baggage is tagged and checked in. In this model, we

simulate the baggage check-in process with three check-in counters represented by a Process

module that has a Check-In Counterresource with a capacity of three. After the bags are checked in,

they are scanned by an automated barcode scanner; which is actually an arrangement of multiple

barcode scanners, scanning from all angles of the baggage in order to find the randomly placed tag.

This automated barcode scanner can usually scan the barcode tags on about ninety percent (90%)

of the bags that go through it [1]. Any baggage that was not scanned by the automated barcode

scanner is routed to another station to be manually scanned by personnel. In the simulation model,

the manual and automated barcode scanners are represented by Process modules while the ninety

percent scan completion rate is simulated by a Decide module with a 2-way by Chance type and a

value of 90 for the Percent True.

Once the bags are scanned, they are routed by a network of conveyors to their appropriate

destination. During this process, the bags are taken through x-ray machines and security devices

such as the Explosive Detection System (EDS), where baggage is checked for explosive material. The

routing conveyors are represented by a single process with resources EDSand Conveyor Beltto

signify the two main parts of the conveyor network. In order to get to their appropriate

destinations, the baggage have to be loaded onto the Destination Coded Vehicle (DCV), which takes

them to their respective terminals and the off-ramp at the gate. The DCV consists of a plastic tub

sitting on a metal cart with wheels that rides on a track using linear induction motors that are

mounted to the track. DCVs possess a passive radio-frequency circuit which broadcasts a unique

number by which individual cars are identified [1]. In the simulation model, it was assumed that the

DCV was a continuation path for the conveyors and as such, could be modeled as a process module

using the same Conveyor Belt resource mentioned above.

At the off-ramp at the gate, there is usually a sorting station at which baggage handlers sort the

bags and load them onto the plane based on whether they belong to transferring (connecting)

passengerspassengers who will be boarding another flight at the current flights destination or

terminating passengerspassengers whose journey ends at the current flights destination. This is

simulated in the model by a Decide module with a 2-way by Chance type and a value of 80 for the

Percent Truesignifying that eighty percent of the travelers will be terminating passengers; an

assumption based on an average of various similar statistics on the subject.

When the plane lands, the baggage follows two separate paths depending on whether it is a

transferring bag or a terminating bag. If it is a transferring bag, it is taken through another series of

routing conveyors where they go through security scans (EDS); and onto another DCV track which

takes them to their appropriate destination. For simplicity, this was modeled by adding a Re-routing


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Conveyorprocess, which is a replica of the Routing Conveyorprocess, and a loop that goes back to

the previously mentioned DCVprocess. If it is a terminating bag, it is taken through a conveyor to

the baggage carousel where the bags reunite with their owners at Baggage Claim. This was modeled

by adding a Conveyor to Carouselprocess which had a resource of Conveyor Beltand went to the

Dispose module Baggage Claim. An algorithm summarizing the simulation process (Conceptual

model) is shown below:

The model was run for twenty-four (24) hours and three hundred and sixty-five (365) days to

simulate the year-round daily operations of an airport. It was also run for 10 replications to reduce

variability in the simulation values. The specification model can be found in the figure below.

Figure 1: Conceptual Model of Airport Baggage Handling System


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Figure 2: Specification Model of Airport Baggage Handling System


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Data Input

In order to obtain necessary distributions to represent process delay times in our model, various

assumptions had to be made. It was assumed that the data acquired from Khadgi [2] was relevant

to our model; and if our model was well constructed, would yield similar (not necessarily the same)

results. This data was collected from the Chicago-Rockford International Airport and was used to

develop appropriate data distributions. However, these distributions were developed using Input

Analyzer and ExpertFitthe data analysis tool used in conjunction with FlexSim simulation software

and had different formatting and parameter assignments than Arena. Hence, it was necessary to

convert them to values that were suitable for Arena based on some calculations and formulas

obtained from the ExpertFit Manual [4]. The relevant distributions from ExpertFit include:

Passenger Inter-Arrival Times: Gamma (2.907407, 38.700486, 1.303549)

Baggage Check-in Times: Expo (44, 86, 1)

Baggage automated scan time: Loglogistic (34.698113, 8.120486, 2.348135) Baggage load in time (assumed to be Plane Loading Time): Weibull (3.933333, 3.199017,

0.744261)

Baggage load out time (assumed to be Sorting Time): Gamma (0.000000, 0.906343,

13.653779)

Manual scan time: Uniform (235, 313)

For each distribution type, the following formulas from [4] were then used to generate Arena-

acceptable parameters:

Exponential distribution

ExpertFit: expo (,)

Parameters: Location (shift) parameter (-,), scale parameter > 0

Mean = +

Arena: Expo (Mean) = Expo ( + )

Gamma distribution

ExpertFit: gamma (, , )

Parameters: Location (shift) parameter (-,), scale parameter > 0, shape parameter, > 0

Arena: Gamma (BETA, ALPHA) = Gamma (, )

Loglogistic distribution

ExpertFit: LL (, , )

Parameters: Location (shift) parameter (-,), scale parameter > 0, shape parameter, > 0


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Mean = +cosecant ()for > 1, where = (/)

Variance = 2{2cosecant (2) [cosecant ()]

2} for > 2, where = (/)

Standard deviation = Variance

Arena: *LOGN (LogMean, LogStd)

*Assuming Lognormal (LOGN) Loglogistic (LL)

Uniform distribution

ExpertFit: U(a,b)

Parameters: a and b are real numbers with a 0, shape parameter, > 0

Arena: WEIB (Beta, Alpha) = WEIB (, )

In order to determine the input parameter values for the conveyor and DCV processes, we assumed

security screening and bag wait times in the conveyor from Hafizogullari et al [3]. In this case study,

wait and process time statistics for baggage screening were developed by TransSolutions LLC for thebaggage handling system at Lambert St. Louis International Airport. It was determined that the Bag

Wait + Process Time for Screeningincludes the time the bag waits while the ID is checked; the

wait time on the EDS belt; the wait time in front of the primary and secondary Explosive Trace

Detection (ETD) agent; the processing time on the EDS and ETD machines, and the secondary ETD

processing time. This parameter was found to have a maximum value of 22.7 minutes; an 85th

percentile value of 3.3 minutes, and a 95th

percentile value of 7.2 minutes.


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Model Verification & Validation

In order to verify our model, we tried to determine that the model operates as intended by viewing

the animation at slow speeds. It was observed that as the time increased in the simulation, the

passenger inter-arrival rate caused some queues at the Baggage check-inandAutomated barcode

scannerprocesses as expected. Also, the Decide modules were operating correctly based on the

intended logic and percentages wanted. Lastly, the feedback loop from the Re-routing conveyoralso

followed the intended logic, sending transferring bags back to the DCVto go through the system

again.

In order to validate the model, the simulation was run according to some cases and scenarios from

the Rockford Airport Case Study [2]. Due to differences in model construction methods and

simulation software, it was not expected that the results will be the same as in Khadgi [2]; however,

it was anticipated that both models will possess similar responses and the results will follow

comparable trends if the model was correct.

For the first case, the simulation was run in three different scenarios to observe the model s

response to changes in baggage screening methods. This was also done to show the effect of

automation to the baggage handling system by comparing the old system of manual baggage

scanning/screening; with the current system of 90% automated scan completion and 10% manual

scanning; and a 100% automated scanning system. As in Khadgi [2], it was noted that the model

showed reduced wait time and increased baggage throughput with the use of more automatic

baggage screening. However, it was also noted that there was an error when 100% of the baggages

had to go through the manual scanner as the number of entities in queue were too large to run in

the Arena Student Edition software. This signifies that the old system of manual scanning wasinefficient and would experience bottlenecks with the current resources; and probably required

more manpower to meet up with passenger inter-arrival demands.

The second case was set up to observe the sensitivity of the model to passenger arrival with varying

numbers of available check-in counters (or agents). The first scenario was run as the current system

with 3 available check-in counters; while the second and third scenarios were run with 4 and 5

available check-in counters, respectively. As in Khadgi [2], the model also followed the trend of

reduced wait times with additional check-in counters.


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Results

Case 1

As was noted in the Verification and Validation section above, the model experienced greater

baggage throughput and improved wait times with the introduction of more automation. It is

also noted that there was an improvement in processing as there were less bags in queue with

the increase of automation. The table below lists the results for the scenarios in Case 1:

Current System Automatic scanner

only

Manual scanner only

Number out 628625 bags 628496 bags N/A

Number of bags auto 565738 bags 628496 bags N/A

Number of bags 62887 bags 0 N/A

Average Wait Time 485.59 seconds 470.47 seconds N/A

Average Total Time 748.05 seconds 705.46 seconds N/A

Max # in baggage 103 bags 82 bags N/A

Check-in counter 0.865 0.8644 N/ATable 1: Results for Case 1

The manual scanner only scenario was unable to be simulated due to entity constraints in the

student edition of Arena. However, it can still be deduced that the wait times would have been

significantly higher and the throughput would have been less than the automated scenarios.

This is because a bottleneck would be experienced at the manual barcode scanner which will

create a backlog in the system and would temporarily bring the system to a halt until the

bottleneck is cleared. Such bottlenecks are the cause for flight and baggage delays and the

undesirable baggage mishandlings that were a common occurrence in the past.


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Case 2

In the second case, the model was analyzed to observe how additional check-in counters would

improve the performance outputs. The system showed significant improvements in baggage

throughput and reduced total and wait times with increased available check-in counters. Also,

the maximum and average numbers of baggage in queue reduced with the addition of more

check-in counters as a result of the improved processing capacity. Lastly, the check-in counter

utilization reduced to acceptable levels with more check-in counters available. The results for

the scenarios of case 2 are shown below:

Current System (3

check-in counters)

4 check-in counters 5 check-in counters

Number out 628625 bags 628764 bags 628599 bags

Number of bags auto

scanned

565738 bags 565956 bags 565716 bags

Number of bags

manual scanned

62887 bags 62809 bags 62885 bags

Average Wait Time 485.59 seconds 285.87 seconds 259.11 seconds

Average Total Time 748.05 seconds 548.18 seconds 521.5 seconds

Max # in baggage

queue

103 bags

(Average15 bags)

68 bags

(Average11 bags)

68 bags

(Average10 bags)

Check-in counter

utilization

0.865 0.6484 0.5187

Table 2: Results for Case 2

Based on the results from the table above, it can be seen that the 4 check-in counter scenario is

the most favorable. In this scenario, the baggage throughput is maximized and the maximum

number in baggage queue is at its minimum (68 bags). Although the wait and total times are

lesser in the 5 check-in counter scenario; they are only minimally so compared to the huge

reduction between the current system and the 4 check-in counter scenario (259.11 seconds