The Optimizing of the Passenger Throughput at an Airport Security Checkpoint

Transcription

1 Open Journal of Applied Siene, 17, 7, ISSN Online: ISSN Print: The Optimizing of the Paenger Throughput at an Airport Seurity Chepoint Xiaoun Mao, Zhiguo Wu Shool of Information, Beijing Wuzi Univerity, Beijing, China How to ite thi paper: Mao, X.C. and Wu, Z.G. (17) The Optimizing of the Paenger Throughput at an Airport Seurity Chepoint. Open Journal of Applied Siene, 7, Reeived: September 4, 17 Aepted: September 6, 17 Publihed: September 9, 17 Copyright 17 by author and Sientifi Reearh Publihing In. Thi wor i liened under the Creative Common Attribution International Liene (CC BY 4.). Open Ae Abtrat In order to inreae hepoint throughput and redue variane in waiting time, the urrent eurity proe of an airport in the United State ha been taen a an example. Depending on the urvey data, we ue the queuing model to review airport eurity hepoint and taffing and to identify potential bottlene whih dirupt paenger throughput. And the method of improving the traffi volume and reduing the waiting time i dedued by the mathematial formula. The influene of the ultural differene of the paenger eurity proe i analyzed by enitivity. Baed on the eurity optimization model, the eurity ytem i put forward to a ertain extent to adapt to different ultural differene. The model tae into aount the impat of parameter hange on the airport eurity proe. Keyword Safety Engineering, Airport Seurity Proe, Queuing Theory, Paenger Throughput, Cultural Differene 1. Introdution In reent year, the number of air traveler i in exploive growth [1], and airport eurity hepoint are ritial omponent in proteting airport and paenger from terrorit threat. But there i a tenion between deire to maximize eurity while minimizing inonveniene to paenger, and the U.S. Tranportation Seurity Ageny (TSA) ha ome under harp ritiim for extremely long line. Paenger have beome more diatified with the eurity reening proe about the longer time they wait to be reened []. And it i unlear that hort waiting time alo mae unexplained and unpredited long line at other airport [3] [4] [5] [6] [7] The Quetion We Fae In order to alleviate the tenion between airport eurity hepoint and pa- DOI: 1.436/ojapp Sep. 9, Open Journal of Applied Siene

2 enger, we developed everal model to figure out the poible bottlene of airport eurity he proe baed on the urrent US eurity proe, whih i hown in Figure 1 [3]. 1.. Our Approah In order to determine the poible bottlene of attered traffi flow from the review of the airport eurity he point and taff, we need to divide eurity hepoint into three mall queuing ytem aording to the A, B, C area baed on the US eurity proe whih i hown in Figure 1, and analyze the bottlene of airport eurity inpetion by uing the queuing theory baed on the data for paenger through eurity proe in the table [3]. Firt, we analyze the urrent eurity proe of the zone A where paenger arrived at the eurity gate and waited in line to he their identity ard and regitration doument. The Zone A (Doument Che) ha two type of entrie: one i Pre-Che Entrane and the other i Regular-Che Entrane Theoretial Knowledge Auming that the airport eurity ytem i in aord with the tandard queuing theory model M/M// /, the operating parameter of the queuing ytem are a follow: The probability of the ytem that i idle: P + (1-1)!!1 ρ Figure 1. Illutration of the TSA Seurity Sreening Proe. DOI: 1.436/ojapp Open Journal of Applied Siene

3 Average queue length: L q ( ρ)!1 ( ρ ) ρ P (1-) The number of utomer in the queuing ytem: L Lq + (1-3) Average waiting time: Lq Wq (1-4) Average time the paenger pent in the queuing ytem: L W (1-5) Auming i the number of eurity hannel at airport, i the ratio of Pre-Che and Regular-Che hannel, the number of Pre-Che Entrane i: C1 (1-6) 1 + The number of Regular-Che Entrane i: 1 C 1 + (1-7) For Zone A: Pre-Che Entrane Aording to the equation in the tandard queuing model M/M// /, the orreponding indexe of the Pre-he Entrane queuing ytem in Zone A are derived a follow: The Probability of Pre-Che Entrane ytem being in idle tate in Zone A: P +!! 1 ρ (1-8) Average queue length in Pre-Che Entrane ervie ytem: ρ ρ ρ P!! 1 ρ ρ Lq + (1-9)!1 ( ρ)!1 ( ρ ) The number of utomer in Pre-Che Entrane ervie ytem: ρ ρ L Lq +!1 ( ρ ) ! +! 1 ρ (1-1) DOI: 1.436/ojapp Open Journal of Applied Siene

4 Average waiting time in Pre-Che Entrane ervie ytem i: W q ρ L ρ q!1 ( ρ) !! 1 ρ Average time pent in the Pre-Che Entrane queuing ytem: ρ ρ L W!1 ( ρ) ! +! 1 ρ (1-11) (1-1) The ame indiator of Regular-Che Entrane ervie ytem an be obtained in Zone A. Aording to the equation in the tandard queuing model M/M// /, the orreponding indexe of the Regular-Che Entrane queuing ytem in Zone A are derived a follow: The Probability of Regular-Che Entrane ytem being in idle tate in Zone A i: P +!! 1 ρ (1-13) Average queue length in Pre-Che Entrane ervie ytem i: ρ ρ ρ Lq P!1!1 ρ ( ρ) ( ρ) 1 1 +!! 1 ρ The number of utomer in Pre-Che Entrane ervie ytem: ρ (1-14) ρ 1 1 L Lq +! + +! 1 ρ (1-15)!1 ( ρ ) DOI: 1.436/ojapp Open Journal of Applied Siene

5 Average waiting time in Pre-Che Entrane ervie ytem i: ρ L ρ q Wq!1 ( ρ) 1 1 +!! 1 ρ (1-16) Average time the paenger pent in the Pre-Che Entrane queuing ytem: ρ ρ L W!1 ( ρ) ! +! 1 ρ (1-17) Sytem ervie trength ρ : ρ (1-18) Subtituting Equation (1-18) into Equation (1-19): L ( ) W! ( ) ! +! (1-19) The variable in Equation (1-19) are, and, i a ontant Parameter Calulation Analyzing the relative information of Pre-Che Entrane in Zone A: a) Nearly 45% of paenger regiter for a projet alled a pre-reening for a reliable paenger [3]. b) Thee paenger need to pay $ 85 for baground he and are eligible for a five-year eparate inpetion proe; Pre-Che paenger and their baggage pa the ame inpetion proe with Regular-Che whih jut mae ome improvement in the peed of the deign ompared to the Regular-Che proe. ) Pre-Che paenger need to remove eletroni and medial equipment and fluid for inpetion without removing hoe, belt, thin jaet, or removing DOI: 1.436/ojapp Open Journal of Applied Siene

6 the omputer from the bag. Set a the average arrival rate of Pre-Che utomer, a the average ervie rate of the utomer, ρ a the ervie intenity; A a the average arrival rate of Pre-Che utomer, A a the average ervie rate of the utomer, ρ A a the ervie intenity; For Pre-Che Entrane: The average arrival time of the utomer: (eond/peron) Average utomer arrival rate: (peron/hour) Average utomer ervie time: Average utomer ervie rate: For Regular-Che Entrane: p (1-) p 396 p h (1-1) The average time of arrival of the utomer: The average utomer arrival rate: Average utomer ervie time: Average utomer ervie rate: p (1-) p 34 p h (1-3) p (1-4) A p 88 p h (1-5) p (1-6) A p 5 p h (1-7) Aumed that the arrival time interval of Pre-Che utomer obey Poion ditribution, whih an be obtained C 1 1, C 3 from Figure 1: Illutration of the TSA Seurity Sreening Proe. Aording to the M/M/C model, we an ee that the Pre-Che queuing ytem in aord with the tandard M/M/1 model: ρ > (1-8) C1 34 Pre-Che Entrane queue will be diharged to infinite, Pre-Che peronnel in TSA will be 1% buy. Similarly, aording to the M/M/C model, we an ee that the regular-he queuing ytem aord with the tandard M/M/ model: ρ 88 A A < C A (1-9) And the probability that the TSA peronnel in Regular-Che in idle tate an be obtained by Equation (1-11): DOI: 1.436/ojapp Open Journal of Applied Siene

7 1 1 1 P + (1-3)!!1 ρ A So P.31, that i Regular-Che in the TSA taff will be 69% of the time in the buy tate, 31% of the time in the idle tate. The operation indexe of regular-he ytem are a follow: Average queue length: ( ρa ) ρa ( ρ ) 3 ( ) ( ) Lq P.31.8 Peron! 1 3! 1.38 A (1-31) Average Captain: L Lq + 1. Peron (1-3) Average waiting time: Average length of tay: Lq.8 Wq 1Seond (1-33).8 L 1. W 15.5 Seond (1-34).8 In Zone B: Baggage and Body Sreening queuing ytem Set B a the average arrival rate of Pre-Che utomer, B a the average ervie rate of the utomer, ρ B a the ervie intenity; Tae the average utomer arrival rate of utomer into the eurity ytem a the average utomer arrival rate of Zone B, whih an be got by um the average utomer arrival rate of Pre-Che Entrane and the average utomer arrival rate of Regular-Che Entrane;Sine baggage he and body he are arried out imultaneouly in Zone B, it i neeary to ue the maximum value between thee two time a the utomer ervie time of B queuing ytem. Beaue the time of baggage he i longer than body he, the mean of the time to get anned property in Zone B i approximated a the average ervie rate of the B queuing ytem B. The average arrival rate of utomer in Pre-Che Entrane and the average arrival rate of utomer in Regular-Che Entrane A an be ued to obtain the average utomer arrival rate of Zone B: B A A 676 p h (1-35) among them 396 p h, 8 p h (1-36) A Thi an be alulated utomer Baggage-Che in Zone B Average ervie time: p (1-37) Average ervie rate: DOI: 1.436/ojapp Open Journal of Applied Siene

8 p 18 p h (1-38) B Aumed Zone B: Baggage and Body Sreening time aord with Poion ditribution, the number of devie an be een from Figure 1 4. Aording to the M/M/C model, we an ee that the queuing ytem in Zone B aord with the tandard M/M/4 model: B 88 ρb.4 > B (1-39) Pre-Che Entrane queue will be diharged to infinite, Pre-Che peronnel in TSA will be 1% of the time in buy tate Conluion 1) In atual life, the proportion of Pre-Che Entrane and Regular-Che Entrane i maller, o the number of Pre-Che Entrane annot meet the need of utomer, whih lead to the phenomenon of infinite queuing; ) Under the aumption that the A Zone queuing ytem meet the tandard of the M/M/ queuing model, the ervie peronnel woring trength alulation how that eurity peronnel of SAT Pre-Che queuing ytem will have been buy. Aording to queuing theory model (1-9) an be een that the average arrival rate of utomer and the average ervie rate are fixed. In order to redue ervie trength an only through hanging the number of C, that i, inreaing the number of eurity peronnel an olve the bottlene of the ervie ytem. 3) In the Zone B, from the Equation (1-39) an be een, to olve the problem of infinite queue need to inreae the value of and B, that i, it need to inreae the number of devie or redue the average ervie time of the paage inpetion.. Method to Improve Paenger Throughput and Redue Variane in Wait Time.1. The Quetion We Fae Two or more potential modifiation to the urrent proe mut be built to improve paenger throughput and redue variane in wait time. And the model mut reflet the influene of the hange of parameter on the airport eurity he proe... Our Approah a) The method to inreae paenger throughput in the eurity he ervie ytem i equal to redue the time of utomer tay in the ytem or inreae the number of eurity hannel, analyze the relationhip between W and other variable; b) The method to redue variane in wait time i equal to redue the average time of utomer tay in the Pre-Che Entrane ervie ytem and Regular- Che Entrane ervie ytem in Zone A, and redue the average time of Bag- DOI: 1.436/ojapp Open Journal of Applied Siene

9 gage Sreening and Body Sreening in Zone B..3. Solving Proe 1) Analyi of the relationhip between W and the variable For the analyi of the hange regularity of W with variable, we tae, a ontant. By the Little equation, analyi of the hange regularity of W with variable i equivalent to the analyi of the hanging rule of L in Equation (1-3) with the variable. Firtly, analyi variation of P with variable, the peifi analyi proe i a follow: Let Then, That i, 1 1 1!!1 ρ ( ) P ( ) P + (-1) P ( + 1) P ( ) +! ( + 1! ) 1 ρ !!1 ρ ! 1 ρ ( + 1)!! ! 1 ρ! ! 1 ρ ( 1) P ( ) (-) P + (-3) Therefore, it an be een that the value of P ha nothing to do with. Seondly, let Then, Then, Q( )!1 ( 1) Q + ( ρ) ρ!1 ( ρ ) + 1!1 ( ) + 1 ( + 1) (-4) (-5) DOI: 1.436/ojapp Open Journal of Applied Siene

10 !1 Q( ) ( + 1) ( + ) + ( + 1!1 ) ( + 1) + 1 Q 1 1 (-6) Diuion and analyi a) when 1, the average arrival rate of utomer in Pre-Che Entrane i:.11 p (one peron per eond, p/),the average utomer ervie rate i:.9 p, tae them into Equation (-6), we got ( ) ( ) ( ) ( + 1) Q Q ( 1) 5.1 > 1 ; + b) when > 1, for ( + 1) >, then > 1, therefore, Q >, obviouly, we an ee Equation (-7) i a monotonially in- Q reaing funtion, A a reult, when ( ) ( ) ( ) ( ) Q Q 4 > 1. Q + 1 Q (-7), we an get the minimize value 4/3, then The average arrival rate of utomer in Pre-Che Entrane i: The average utomer ervie rate i: We an get, Conluion.11 p ;.9 p ; ( ) ( + 1) Q Q > 1. Sine Q( ) i a monotonially dereaing funtion, it an be een that the value of P ha nothing to do with. Therefore, funtion with variable. Additionally, L q i monotone dereaing Lq 1 W + (-8) Then we an get W i monotone dereaing funtion with variable. Therefore, inreaing the number of that the time of utomer tay in the ervie ytem an be redued, whih an inreae the paenger throughput in DOI: 1.436/ojapp Open Journal of Applied Siene

11 Pre-Che Entrane ervie ytem in Zone A. ) Analyi of the relationhip between W q and the variable For Lq Wq (-9) We an get, W q i monotone dereaing funtion with variable. Therefore, inreaing the number of TSA agent, the average waiting time of the utomer in the ytem an be hortened. 3) Analyi of the relationhip between W and the variable For the analyi of the hange regularity of W with variable, tae, a one ontant. By the Little equation, analyi of the hange regularity of W with variable i equivalent to the analyi of the hanging rule of L in Equation (-3) with the variable. Firtly, analyi variation of P with variable, the peifi analyi proe i a follow: a) when 1, it i atified the tandard queuing theory model M/M/1/ /, for the Little equation, W 1 (-1) We an get that b) when > 1, Q ( ) W i monotone dereaing funtion with variable. P ( ) (-11)!!1 ρ 1 ( ρ) ρ +!1 ( ρ )!1! (-1) Obviouly, Q ( ) i a monotone dereaing funtion with repet to the variable. Aording to Equation (-1), Q( ) P ( ) 1 W + (-13) and ( ) P ( ) Q 1 W + ( ρ) ρ 1 + (-14)! ( 1 ρ) ( 1 )! ρ ! 1 ) 1! + DOI: 1.436/ojapp Open Journal of Applied Siene

12 Let mae >, h h ( ) 1! 1 + 1! 1! 1 1 +! ( ) h( ) for >, o > and Let Then, 1! 1 1 +! 1! 1 1 +! 1 1 1! 1! g ( ) 1! > (-15) (-16) (-17) g ( ) 1 g( ) 1 1 ( ) (-18) Beaue, >, 1, o that, >, then, > 1 and 1 < g ( ) for ( ), o that > 1, h( ) h( ) >, then we an g ( ) get h ( ) i an inreaing funtion, taing ( ) now that h to the equation W,we W i a monotone dereaing funtion with repet to the variable L i a monotone dereaing funtion with repet to. Whih alo mean the variable. Therefore, reduing the average ervie time of the utomer in the ervie ytem i equivalent to inreaing the throughput of utomer in Pre-Che Entrane ervie ytem in Zone A. 4) To analyi the relationhip between W q and the variable in the imilar way. For Lq Wq (-19) We an get W q i a monotone dereaing funtion with repet to the varia- DOI: 1.436/ojapp Open Journal of Applied Siene

13 ble. Thu reduing i to redue the average waiting time of the utomer in the ervie ytem..4. Conluion For Zone A:Regular-Che Entrane 1) In Regular-Che Entrane ervie ytem, inreaing the number of SAT agent or reduing the average ervie time an redue the time of utomer tay in the ervie ytem, and inreae the throughput of utomer in Regular- Che Entrane ervie ytem. ) For the average ervie time for utomer in Pre-Che Entrane ervie ytem i le than the Regular-Che Entrane ervie ytem, we an inreae the number of SAT agent or reduing the average ervie time in Regular- Che Entrane to redue the waiting time variane between Pre-Che Entrane ervie ytem with Regular-Che Entrane ervie ytem. For Zone B:Baggage and Body Sreening Aording to equation of the tandard queuing model M/M// /, the orreponding indexe of the Regular-Che Entrane queuing ytem in Zone B are derived a Equation (1-1)-(1-5); The following onluion an be drawn: 1) For the Baggage and Body Sreening ervie ytem in Zone B, It an be proved that W and W q are monotone dereaing funtion of variable ; inreaing the number of their hepoint equipment in Zone B an redue the tay time of utomer in the ervie ytem, whih an inreae the throughput of utomer in Baggage and Body Sreening ervie ytem in Zone B; ) To redue the average waiting time variane between Baggage Sreening ervie ytem with Body Sreening ervie ytem, we an inreae the number of hepoint equipment (or SAT agent) or redue the average ervie time for eah utomer. 3) For the Baggage and Body Sreening ervie ytem in Zone B, we tae, a a ontant in Equation ((1-1), (1-) and (1-5)), W and W q are monotone dereaing funtion about the variable ; in order to inreae the throughput of utomer in Baggage and Body Sreening ervie ytem, we an redue the average ervie time for utomer. 3. The Influene of Culture Variane Aording to equation of the tandard queuing model M/M// /, the orreponding indexe of the Regular-Che Entrane queuing ytem in Zone A and Zone B are alulated, we tae part of variable in Equation (1-19) a a ontant whih value are given in 1.4 Parameter Calulation, while analyzing the relationhip between W and variable and in different ituation. Then we ue Matlab oft to how the relationhip between the average ervie rate of the utomer and the average tay time of the utomer in the eurity ytem in the ae of hange in the number of eurity peronnel and the relationhip be- DOI: 1.436/ojapp Open Journal of Applied Siene

14 tween the average tay time of paenger in the ytem he and regular-he. W and the ratio of pre- Senitivity Analyi Figure how the relationhip between the average ervie rate μ of the utomer and the average tay time of the utomer in the eurity ytem in the ae of hange in the number of eurity peronnel. From the hart one, we an ee that when the number of eurity peronnel i a fixed value, utomer average tay time in eurity ytem dereae with the inreae of average utomer ervie rate. When inreae, the average tay time dereaed. When inreaed from 4 to 8, the average utomer taying time dereaed obviouly. However, when inreaed from 8 to 1, the average taying time of the utomer i baially unhanged. Thi an be een: 1) The inreae of average ervie rate i inverely proportional to the average tay time of utomer, that i, the average ervie time of utomer i proportional to the average tay time. Therefore, reduing the average ervie time of utomer an ignifiantly redue the average tay time of utomer in the ytem and inreae the utomer throughput of the airport. ) When i mall, inreaing the value of an ignifiantly redue the ervie time of utomer in the ytem; when i large, the inreae ha little effet on tay time of utomer. From a pratial point of view, we an ee that when the number of eurity peronnel i little, eurity peronnel ha le free time. When the reahe a ertain value, the inreae of the number of eurity peronnel will aue redundant peronnel, high eurity peronnel idle rate, whih reult in ot lo to the airport. Figure 3 how the relationhip between the average tay time of paenger in the ytem W and the ratio of pre-he and regular-he. A an be een from the figure, and W i inverely proportional. So the paenger of regularhe eurity inpetion mode into the hannel of pre-he ae an redue the average tay time of utomer in the eurity ytem effetively. Figure. Senitivity analyi. DOI: 1.436/ojapp Open Journal of Applied Siene

15 Figure 3. Senitivity analyi. From the above, we an ee that the rate of W dereae i fater when the i hanged from to 1; When W hanged from 1 to 3, the rate of deline lowed down. Therefore, when the reahe a ertain value, inreaing the proportion of pre-he eurity he ha little effet on the redution of the average tay time of paenger in the ytem. From the atual ituation, it ha been able to meet the need of the number of utomer whih aept traveler he pre trut plan when the pre-he eurity hannel i enough. At thi point, the inreae of pre-he eurity hannel will aue the inreae idle probability of pre-he eurity hannel, whih i not onduive to improving the airport utomer throughput. 1) Conider the olletive effiieny a the priority priniple Queuing model with olletive effiieny for priority onideration mainly aim to inreae the number of paenger whih i handled by eurity he. Aording to the onluion of the eond quetion, inreaing the number of eurity equipment, the number of eurity peronnel, and redue equipment ervie time an meet the orreponding demand. ) Conider private pae a the priority priniple The main differene between normal queuing model and the queuing model onidering private pae a priority priniple i the ditane between people i longer. Thu, inreaing the utomer wal ditane i equivalent to inreae the average queue length of utomer in the ytem and the time utomer through eurity hepoint ot. From Equation (-8) an be een, W i a monotonially inreaing funtion of variable. Therefore, thi i tantamount to reduing the paenger throughput. Speifi repone meaure a) Inreae the number of devie and eurity peronnel b) Redue ervie time of equipment ) Conider the individual effiieny a a priority priniple Conider peronal effiieny a priority priniple equivalent to the utomer ue pre-he eurity mode more frequently. It an be een that inreaing the DOI: 1.436/ojapp Open Journal of Applied Siene

16 number of eurity peronnel of pre-he eurity and eurity equipment and improve the proportion of pre-he hannel an improve the effiieny of individual effetively aording to the eond quetion (the number of). The objetive funtion (1) i to minimize the travel time, () i to minimize the number of death among all the refugee. Contraint (3) are the variable initialization. Contraint (4) enue that all the refugee in the Middle Eat depart uefully. Contraint (5) determine the death in the flow of refugee. Contraint (6) (7) and (8) impoe the refugee whoe aylum requet are not be approved, and have to leave the urrent ountry to another available ountry. Contraint (9) retrit the total number of the refugee in Europe, and the number of the refugee roing in urrent ountry, and refugee reettled are limited by Contraint (1) and (11). Contraint (1) define the ale of the refugee and ontraint (13) enure the range of parameter and variable. 4. Poliy and Proedural Reommendation Several Reommendation 1) The enourage poliy to Pre-he eurity he mode a) Popularize the nowledge of pre-he b) Redue pre-he eurity ot ) Improve the proportion of pre-he hannel ) Inreae the number of eurity equipment and ervie peronnel, redue eurity equipment ervie time 3) Redue the length of ervie hannel ae and weanee, and propoe idea for improvement (future wor). 5. Strength and Weanee 5.1. Strength Eah model i diued by uing ientifi and aurate method and the relationhip between eah parameter and eah variable i obtained. Thi paper mae a enitivity analyi of the utomer tay time in the ytem, and diue the influene of eah variable on the W and explained the reaon of thi effet from the pratial point of view 5.. Weanee We only onider the relationhip between eah parameter and the individual variable by etting other variable a fixed value. But in the atual ituation, eah variable i ontantly hanging, and they have a better ombination to optimize the parameter. Thi paper ha not diued their ombinatorial optimization, o that the onluion i non-optimal and we need to trengthen the reearh of thi apet in the future wor. 6. Conluion In order to inreae hepoint throughput and redue variane in waiting time, DOI: 1.436/ojapp Open Journal of Applied Siene

17 we divide the urrent proe of a US airport eurity hepoint into two phae: Zone A for Doument Che by Pre-Che Entrane or Regular-Che Entrane, Zone B for Baggage and Body Sreening. Firt, we analyze the type of the airport eurity proe of the two phae, inluding the utomer reah time interval ditribution and ervie time ditribution. Seond, we he out the effet of a variable on objetive funtion by et other variable to a fixed value. Through the tet reult, we an find the bottlene of the eurity he proe and tae orreponding meaure to improve the paenger throughput and redue utomer waiting time variane. Third, we diu the effet of national ultural differene on the proe of paage pa through hepoint. And we give the orreponding uggetion to inreae utomer throughput to redue the impat of ultural differene. Finally, we put forward to trategie and meaure that apply to the world in order to optimize airport eurity ytem proe. Through the analyi of the airport eurity proe, we an provide referene for the airport to improve utomer throughput. We will further improve the paper to meet the atual eurity proe better. Referene [1] Ma, J.J. (16) Virtual Queuing in Civil Aviation Seurity Sytem. Tehnology and Innovation, No. 19, [] Lee, A.J. and Jaobon, S.H. (11) Optimizing the Aviation Chepoint Proe to Enhane Seurity and Expedite Sreening. Wiley Enylopedia of Operation Reearh and Management Siene, John Wiley & Son, In. [3] [4] Zeng, J.J. (9) Airport Seurity Setting and Optimization. Knowledge-Baed Eonomy, No. 1, [5] Cheng, J.Y. and Yang, X. (16) Refletion on the Airport Seurity Management Mode in China. Chinee Publi Seurity (Aademi Edition), No., [6] Anonymou. Tranportation Seurity Adminitration; TSA Announe $6.9 Million Award for New Cheed Baggage Sreening Sytem at Wihita Mid-Continent Airport. Bioterrorim Wee, 9. [7] Anonymou. Morpho Detetion Cheed Bag EDS Meet ECAC Approval for Ue by EU Airport. Manufaturing Cloe-Up, 11. DOI: 1.436/ojapp Open Journal of Applied Siene

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