Enhancing complex system performance using discrete-event simulation

Enhancing Complex System Performance Using Discrete-Event Simulation Glenn O. Allgood, Mohammed M. Olama, and Joe E. Lake Computational Sciences and Engineering Division Oak Ridge National Laboratory PO BOX 2008, MS 6085 Oak Ridge, TN 37831, USA [email protected], [email protected], [email protected]

Keywords: Discrete event simulation, queuing model, potential capacity Abstract In this paper, we utilize discrete-event simulation (DES) merged with human factors analysis to provide the venue within which the separation and deconfliction of the system/human operating principles can occur. A concrete example is presented to illustrate the performance enhancement gains for an aviation cargo flow and security inspection system achieved through the development and use of a process DES. The overall performance of the system is computed, analyzed, and optimized for the different system dynamics. Various performance measures are considered such as system capacity, residual capacity, and total number of pallets waiting for inspection in the queue. These metrics are performance indicators of the system’s ability to service current needs and respond to additional requests. We studied and analyzed different scenarios by changing various model parameters such as the number of pieces per pallet ratio, number of inspectors and cargo handling personnel, number of forklifts, number and types of detection systems, inspection modality distribution, alarm rate, and cargo closeout time. The increased physical understanding resulting from execution of the queuing model utilizing these vetted performance measures identified effective ways to meet inspection requirements while maintaining or reducing overall operational cost and eliminating any shipping delays associated with any proposed changes in inspection requirements. With this understanding effective operational strategies can be developed to optimally use personnel while still maintaining plant efficiency, reducing process interruptions, and holding or reducing costs. 1.

INTRODUCTION Many (enterprise) systems cannot be modeled as Markov processes due to a non-deterministic behavior that is elicited as a result of instantaneous changes in revenue streams brought about by desires to either exceed customer expectations or take advantage of opportunities to increase short-term revenue and profits. These actions change the system’s historical dependencies and are reflected in

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differences in current state-space projections. While on the surface, these actions may seem cost effective, investigations have shown that in the long run if left unchecked they introduce process imbalances into the system. To eliminate these effects, control actions must be instituted to negate them or they are left to work their way through the system creating undesired consequences for the process. These actions and/or decisions are not necessarily tied to any resource restrictions or environmental influences but are based solely on market push and economics. The impact of such decisions introduces process bifurcations that can reduce work efficiency requiring reallocation of personnel. To better understand these impacts an approach must be taken that allows for the study and analysis of the nonlinear interactions that exists between the system, the context within which it operates, and the personnel who manage and control it. With this understanding, managers can anticipate impacts and develop and implement allocation strategies that minimize process disturbances and system risk. In this paper, we utilize discrete-event simulation (DES) merged with human factors analysis to provide the venue within which the separation and deconfliction of the system/human operating principles can occur. By developing a DES model of a process a manager and/or plant operations engineer can identify and understand the nonlinearities that create process imbalances and use this information to match current resource allocations with subscribed resource allocations resulting from the economic decisions. With this understanding effective operational strategies can be developed to optimally use personnel while still maintaining plant efficiency, reducing process interruptions, and holding or reducing costs. A capability such as described above would not only provide support for real time (tactical) process needs but would also be used to develop resource allocation plans from a strategic perspective. A concrete example is presented to demonstrate the method’s viability. It illustrates the performance enhancement gains for an aviation cargo flow and security inspection system achieved through the development and use of a process DES. The overall performance of the system is computed, analyzed, and optimized for the

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different system dynamics. Various performance measures are considered such as system capacity, residual capacity, and total number of pallets waiting for inspection in the queue. These metrics are performance indicators of the system’s ability to service current needs and respond to additional requests. We studied and analyzed different scenarios by changing various model parameters such as the number of pieces per pallet ratio, number of inspectors and cargo handling personnel, number of forklifts, number and types of detection systems, inspection modality distribution, alarm rate, and cargo closeout time. The increased physical understanding resulting from execution of the queuing model utilizing these vetted performance measures identified effective ways to meet inspection requirements while maintaining/reducing overall operational cost and eliminating any shipping delays associated with any proposed changes in inspection requirements. SYSTEM DYNAMICS DES is characterized as stochastic, dynamic, and discrete-time system model. In DES, the operation of a system is represented as a chronological sequence of events [1]. Each event occurs at an instant in time and marks a change of state in the system. Also, in a DES there is no time loop. But, there are events that are scheduled. So, at each run step, the next scheduled event with the lowest time gets processed. And the current time is then the time that event is supposed to occur. Therefore, in DES, we have to keep the list of scheduled events sorted (in order). DES provides computational advantage over other simulators, and therefore it is recommended for modeling large complex systems. In this paper, DES is employed in modeling an aviation cargo flow and security inspection system. There are various system processing steps required to service cargo in major airport facilities due to the large amount of cargo [2]. These steps can be categorized into Accept (Pre-Inspect), Transit, Inspect, Consolidation, and Loading. Also, there are various commodity types that require different servicing and handling by these processes. The commodity types are divided into five categories: Dash, Domestic P1, Equation, International, and Pet First. By considering the overall cargo process flow system as a unit, the overall system dynamics can be described by four critical operating parameters: the initial total number of pieces (P) (in Pieces), turn time (T) (in Min), overall system latency (τ) (in Min), and overall average system service rate (σ) (in Pieces/Min). The overall system latency is defined as the time required for the first piece to be serviced and loaded into a wagon for movement from the facility to the aircraft. The overall average system service rate is the estimated depletion rate (extraction rate) of cargo as it is removed from the inspection process. These system dynamics are shown in Figure 1. Note that the residual

capacity of the system (in Pieces) is described by the intersection of the average system service rate line with turn time, and the optimal case (residual capacity = 0) occurs when all pieces finish servicing exactly at the turn time. By observing the relationships among these system dynamics (see Figure 1), the number of cargo pieces (parcels) required servicing in an airport facility, denoted by y, is described by P , t ≤τ ⎧ ⎪ P PT ⎪ y = ⎨− t+ ,τ ≤t ≤T T −τ ⎪ T −τ ⎪⎩ 0 , t ≥T

(1)

where t is time, and P / T − τ is the slope which is the optimal σ. The residual capacity (RC) is given by RC = σ (T − τ ) − P

2.

(2)

Thus, for a given system dynamics, you can tell whether the system is able to service all the cargo pieces on or before the scheduled turn time by computing the RC in equation (2). Simulation models for the various system processes that are used to estimate the overall service rate and system latency and thus determine the overall system performance are discussed in the next section.

Figure 1. Overall system dynamics.

3.

DESIGN AND SIMULATION FOR AN AVIATION CARGO FLOW AND INSPECTION SYSTEM

In this section, we consider the design and modeling of an aviation cargo flow and inspection system. Cincinnati/Northern Kentucky International Airport (CVG) is chosen as a facility to run and validate the developed models and analysis [3]. CVG is a Delta Air hub and has the second largest number of daily flights in Delta’s route system. Onsite measurements are collected in CVG airport facility to validate the queuing model.

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3.1 Design and simulation for the Accept process There are two different types of Accept processes associated with commodity types. One associated with Dash and one associated with the class called others. Dash is handled differently due to its premium value. It has a more direct path to inspection and is modeled as a direct queue from acceptance to inspect without the intervening steps identified in Figure 2.

movement. These values can be mapped linearly to various numbers of FLs as follows: F 2 * WTransit = WTransit

2 F

(4)

where F is the number of FLs (admissible values are 1, 2, 3, 2 is the average transit time for 2 FLs (given in 4), WTransit F is the average transit time for F FLs. Figure 3), and WTransit Note that the values described in Figure 3 are expected values for GRVs and as previously state, determined from actual measurements taken at the facility.

Figure 2. Flow diagram of the Accept process for all but Dash commodity type. The proposed process flow for the class - others consists of door open, door closed, accept time, first drop, first move, last AWB applied, and last transit move. When a shipment arrives, there is a waiting time of about 3 minutes on average till the first drop of a pallet. This time is needed to allow the driver of the truck and the acceptance personnel to prepare all the required paper work and arrange the commodity in the truck to be unloaded. The process of unloading the truck is characterized by an average service rate of 0.85 Min/Pallet where a commodity is queued to pallets. During the unloading process, an AWB is created and applied to each commodity and has an assigned service rate modeled as a Gaussian random variable (GRV) with mean 5 Sec/Item and standard deviation (SD) of 1 Sec/Item. The model of the Accept dynamics is shown in Figure 2. Note that the values of these model parameters are determined from actual measurements taken at the facility and are represented as GRV with certain mean and SD. The other type of the Accept process (for Dash commodity type) is modeled as a waiting time (in Min) that satisfies the following relation: ; PDash ≤ 3 ⎧⎪5 WDash = ⎨ ⎪⎩5 + ( PDash − 3) * 0.75 ; PDash ≥ 3

(3)

where WDash is the average waiting time and PDash is the total number of arrived Dash parcels. 3.2 Design and simulation for the Transit process The three different kinds of Transit processes are presented in Figure 3, in which the average transit times are based on two forklifts (FLs) being used for cargo

Figure 3. Process flow chart for the different Transit processes. 3.3 Design and simulation for the Inspect process Several methods are being used to screen 100% of checked baggage. The most common methods used in CVG involve electronic screening either by an Explosives Detection System (EDS) or Explosives Trace Detection (ETD) devices. The EDS machines are the large machines that can be over 20 feet long and weigh up three tons. Baggage is loaded on a conveyor belt feed system to the EDS machine. If an alarm occurs, the bag will require further inspection by an ETD machine. The ETD machines are much smaller machines and are the primary machines used in many airports, including CVG. Baggage screened with an ETD machine is accomplished by a screener swabbing a bag and presenting this to the ETD machine for analysis. For the CVG discrete event model two ETD and one EDS machine was used. However, it is a simple process extended to include more machines for planning and operational analysis. The ratio of the amount of cargo that will be inspected by the ETD machines with respect to the amount of cargo that will be inspected by the EDS machine is called ETD/EDS distribution, and plays an important role in the performance of the Inspection process as well as the overall system as will seen in Section 4. Also, different commodity types are handled differently in the Inspection process. For example, Dash was usually inspected by ETD machines due to its small size (the model of the ETD machine allowed for size restrictions). Figure 4 shows a flow diagram for the Dash commodity type inspection process using an ETD machine. Note that all the

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parameter values in Figure 4 are per pallet basis and represent the expected value for the GRVs.

Figure 6. Flow diagram for a full parameter set EDS Inspect process.

Figure 4. Flow diagram of the ETD Inspect process for Dash commodity type. The flow diagram for the class - others - using two ETD machines is shown in Figure 5. It is similar to Dash but with different operational parameter (service rates and time delays) values. Notice here that time delays and service rates (in Min/Pallet) are higher than the ones for Dash since the commodity in this case are bigger in size and weight and are serviced on a pallet basis. Also notice that there is an alarm after the inspection service block. When an alarm occurs, the commodity requires further handling and inspection as represented in the feedback loop in Figures 4 and 5. As stated earlier, alarm rate plays an important role in determining overall system performance and is discussed in Section 4. It is worth mentioning that the switch shown in Figure 5 is used for load balancing of the ETD machines. A new pallet leaves the queue to the inspect process when the inspect service block is cleared.

Figure 7. Flow diagram for a reduced parameter set EDS Inspect process. 3.4 Design and simulation for the Drop Zone process Figure 8 shows the drop zone process. It consists of two events: Time delay for latency unload from the facility and loading server. Note again that all the parameter values are the expected values calculated for the GRV values associated with facility measurements.

Figure 8. Flow diagram for a Drop Zone process. 3.5 Overall system The flow diagram of the overall process, which integrates all previously described sub-processes, is shown in Figure 9. 4. Figure 5. Flow diagram of the ETD Inspect process for all but Dash commodity types. There are two kinds of modeled EDS machines: Full and reduced parameter sets. The flow diagram of the full and reduced parameter set EDS machines are shown in Figures 6 and 7, respectively. The reduced parameter EDS machine is modeled to accommodate cargo that doesn’t need reconstitution, stretch wrap, and other processes. Our simulation model considers both cases. The application process block represents the processes of apply SID, using ADAS (a data enterprise system) to complete database logical activities, placement, and logical decisions.

ANALYSIS AND SIMULATION RESULTS In this section, system performance under various scenarios is computed using the simulation model presented in Section 3. SimEvents 2.3 toolbox [4], which extends the Simulink product in Matlab [5] with a discrete-event simulation model of computation, is used to build the model. Simulation results for various scenarios are presented next. Scenario 1: Effect of ETD/EDS distribution on time duration of all processes

Variable parameters: •

100% ETD, 75% ETD / 25% EDS, 50% ETD / 50% EDS, 25% ETD / 75% EDS, 100% EDS

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Table 1. Numerical results for the effect of ETD/EDS distributions on time duration of various processes.

100% ETD

75% ETD / 25% EDS

6.7062

5.9507

2.1355

1.5869

0.91457

System Latency 15.2956

15.2956

15.2956

15.2956

15.2956

43.1763

55.4473

103.4268

125.1803

222.1966

System Service Rate

50% ETD 25% ETD / 50% / 75% EDS EDS

100% EDS

Overall Time Door Open 2 First Apply AWB Time First Apply AWB 2 Leave Ingest Time

23.376

23.376

23.376

23.376

23.376

21.3714

21.3714

21.3714

21.3714

21.3714

Accept Time

24.3474

24.3474

24.3474

24.3474

24.3474

Transit Time ETD 1 Inspect Time ETD 2 Inspect Time EDS Inspect Time ETD 1 Consolidation Time ETD 2 Consolidation Time EDS Consolidation Time

20.6573

20.6573

20.6573

20.6573

20.6573

28.1878

23.6309

18.903

18.6305

0

26.2126

22.5784

18.3261

17.4508

0

0

41.958

89.5886

115.0031

210.373

36.3148

31.2396

26.1617

25.8646

0

34.6809

30.187

26.1586

23.8798

0

0

43.7417

91.7292

113.4827

210.499

Scenario 2: Effect of alarm rate and ETD/EDS distribution under 3 TSA inspectors on the overall performance of the system Variable parameters: Figure 9. Overall system flow diagram. Fixed parameters: • • • • • •

200 Pieces International 8 Pieces/Pallet 2 ATS forklift drivers 3 TSA inspectors (2 for ETD and 1 for EDS) 1% alarm rate Reduced parameter set in EDS

Table 1 shows simulation results of effects on system timing requirements of each subsystem as a function of inspection modality distribution. Notice that 100% ETD inspection allotment is the most efficient among all since it has the lowest overall service time.

• • •

100% ETD, 75% ETD / 25% EDS, 50% ETD / 50% EDS, 25% ETD / 75% EDS, 100% EDS 1%, 15%, and 25% Alarm Rates Reduced/Full parameter set in EDS

Fixed parameters: • • • •

200 Pieces Domestic P1 8 Pieces/Pallet 2 ATS forklift drivers 3 TSA inspectors (2 for ETD and 1 for EDS)

Figure 10 shows system performance (described in total number of pieces (or parcels) required servicing in the system as a function of time, and total number of pallets in the inspection queue waiting for inspection) for various alarm rates (1%, 15%, and 25%) under 100% ETD

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inspection allotment and reduced parameter set in EDS. Due to limited space, simulation results for the other inspection modality distributions under reduced/full parameter set in EDS are not presented.

simulation results for the other inspection modality distributions are not presented. Scenario 4: Effect of alarm rate and ETD/EDS distribution under 4 TSA inspectors on the overall performance of the system

Scenario 3: Effect of alarm rate and ETD/EDS distribution under 2 TSA inspectors on the overall performance of the system

Variable parameters:

Variable parameters: •

•

100% ETD, 75% ETD / 25% EDS, 50% ETD / 50% EDS, 25% ETD / 75% EDS, 100% EDS 1%, 15%, and 25% Alarm Rates

•

•

Fixed parameters:

Fixed parameters: • • • • •

• • • • •

200 Pieces Domestic P1 8 Pieces/Pallet 2 ATS forklift drivers 2 TSA inspectors (1 for ETD and 1 for EDS) Full parameter set in EDS

200

200

150

100

200

100

50

10

20

30

40

100

Service Rate = 1.615 Pieces/Min System Latency = 32.5665 Min

System Latency = 32.5665 Min 0

0

150

50

Service Rate = 2.7377 Pieces/Min

System Latency = 32.5665 Min 0

Number of Pieces Required Servicing

250

150

Service Rate = 3.9193 Pieces/Min

50

Number of Pieces Required Servicing

Number of Pieces

250

Number of Pieces

Number of Pieces

Number of Pieces Required Servicing

200 Pieces Domestic P1 8 Pieces/Pallet 2 ATS forklift drivers 4 TSA inspectors (3 for ETD and 1 for EDS) Full parameter set in EDS

Figure 12 shows system performance for various alarm rates (1%, 15%, and 25%) under 100% ETD inspection allotment and full parameter set in EDS. Due to limited space, simulation results for the other inspection modality distributions are not presented.

Figure 11 shows system performance for various alarm rates (1%, 15%, and 25%) under 100% ETD inspection allotment and full parameter set in EDS. Due to limited space,

250

100% ETD, 75% ETD / 25% EDS, 50% ETD / 50% EDS, 25% ETD / 75% EDS, 100% EDS 1%, 15%, and 25% Alarm Rates

50

60

70

80

0 0

10

20

30

40

50

60

70

80

90

100

0

20

40

60

Number of Pallets in Inspect Q

Number of Pallets in Inspect Q

10

80

100

120

140

Time (min)

Time (min)

Time (min)

Number of Pallets in Inspect Q

15

15

9

6 5 4 3

Number of Pallets

7

Number of Pallets

Number of Pallets

8

10

5

10

5

2 1 0 20

25

30

35

40

Time (min)

(a)

45

50

55

60

0 20

30

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60

Time (min)

(b)

70

80

90

0 20

40

60

80

100

120

140

Time (min)

(c)

Figure 10. System performance for Scenario 2, described in total number of pieces required servicing in the system as a function of time (top), and total number of pallets in the inspection queue waiting for inspection (bottom) for (a) 1%, (b) 15%, and (c) 25% alarm rate.

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Number of Pieces Required Servicing

150

100

Service Rate = 1.9603 Pieces/Min

50

20

40

60

150

100

Service Rate = 1.0219 Pieces/Min

50

Service Rate = 1.5988 Pieces/Min

System Latency = 39.7372 Min 0

25% Alarm Rate 2 ATS FL Drivers 1 TSA Insp for ETD and 1 TSA Insp for EDS

200

Number of Pieces

Number of Pieces

Number of Pieces

100

50

15% Alarm Rate 2 ATS FL Drivers 1 TSA Insp for ETD and 1 TSA Insp for EDS

200

150

System Latency = 39.7372 Min

System Latency = 39.7372 Min

80

100

120

0

140

0

20

40

60

Time (min)

80

100

120

140

0

160

0

Number of Pallets in Inspect Q

16

16

14

14

14

10 8 6

12 10 8 6

10 8 6

4

4

2

2

2

50

60

70

80

90

0 20

100

Time (min)

250

12

4

40

200

18

Number of Pallets

Number of Pallets

16

12

150

Number of Pallets in Inspect Q

18

30

100

Time (min)

18

0 20

50

Time (min)

Number of Pallets in Inspect Q

Number of Pallets

250

250

1% Alarm Rate 2 ATS FL Drivers 1 TSA Insp for ETD and 1 TSA Insp for EDS

200

0

Number of Pieces Required Servicing

Number of Pieces Required Servicing

250

40

60

80

100

120

0 20

140

Time (min)

(a)

40

60

80

100

120

140

160

180

200

Time (min)

(b)

(c)

Figure 11. System performance for Scenario 3, described in total number of pieces required servicing in the system as a function of time (top), and total number of pallets in the inspection queue waiting for inspection (bottom) for (a) 1%, (b) 15%, and (c) 25% alarm rate. Number of Pieces Required Servicing

250

100

150

100

50

Service Rate = 5.7935 Pieces/Min

50

0

10

20

30

40

100

Service Rate = 1.9952 Pieces/Min

System Latency = 30.1523 Min 50

60

0

70

0

10

20

30

40

50

System Latency = 30.1523 Min 60

70

80

0

90

0

20

40

Time (min)

Time (min)

4

150

50

Service Rate = 3.5059 Pieces/Min

System Latency = 30.1523 Min 0

25% Alarm Rate 2 ATS FL Drivers 3 TSA Insp for ETD and 1 TSA Insp for EDS

200

Number of Pieces

150

Number of Pieces Required Servicing

250

15% Alarm Rate 2 ATS FL Drivers 3 TSA Insp for ETD and 1 TSA Insp for EDS

200

Number of Pieces

200

Number of Pieces

Number of Pieces Required Servicing

250

1% Alarm Rate 2 ATS FL Drivers 3 TSA Insp for ETD and 1 TSA Insp for EDS

Number of Pallets in Inspect Q

Number of Pallets in Inspect Q

10

3.5

60

80

100

120

Time (min) Number of Pallets in Inspect Q

10

9

9

8

8

7

7

2.5 2 1.5

Number of Pallets

Number of Pallets

Number of Pallets

3

6 5 4 3

1 0.5 0 20

25

30

35

Time (min)

(a)

40

45

6 5 4 3

2

2

1

1

0 20

30

40

50

60

70

80

0 20

30

40

50

60

70

Time (min)

Time (min)

(b)

(c)

80

90

100

110

Figure 12. System performance for Scenario 4, described in total number of pieces required servicing in the system as a function of time (top), and total number of pallets in the inspection queue waiting for inspection (bottom) for (a) 1%, (b) 15%, and (c) 25% alarm rate.

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5. 480

500

460

480 440

460 Potential Capacity

440 420

420 400

400

380 360

380

340 360

320 300 6

5 5 4

320 3

Number of TSA Inspectors

340

4 3

Number of ATS Personnel

Figure 13. System’s potential capacity as a function of number of TSA and ATS personnel. From Figures 10, 11, and 12, you can see how the overall service rate is reduced and the number of pallets waiting for inspection is increased as the alarm rate increases; and how the overall system performance improves by increasing the number of TSA inspectors. The potential capacity of the system for 100% ETD inspection allotment as a function of number of TSA and ATS personnel is shown in Figure 13. Notice that there is a big jump in the potential capacity when increasing the number of TSA inspectors from 3 to 4. Figure 14 shows the potential capacity as a function of inspection modality distribution and cargo closeout time under 8 parcels/pallet ratio. Notice that the best performance among all occurs under 100% ETD inspection allotment, and also notice that the potential capacity of the system is proportional to cargo closeout time.

CONCLUSION Performance evaluation of a discrete event cargo inspection system is conducted and analyzed. We studied and analyzed different scenarios by changing various model parameters such as the number of pieces per pallet ratio, number of inspectors and cargo handling personnel, number and types of detection systems, inspection modality distribution, alarm rate, and cargo closeout time. These data, in turn, are used to analyze optimal performance regimes in a facility based on varying system dynamics. The increased physical understanding resulting from execution of the queuing model utilizing these vetted performance measures identified effective ways to meet inspection requirements while maintaining or reducing overall operational cost and eliminating any shipping delays associated with any proposed changes in inspection requirements. This model can be employed in a real time environment to manage cargo flow and inspection or as an off-line pre- or post processing tool. With the addition of a cost model, the application can be used effectively to reduce overall shipping cost by balancing resource needs and anticipating system surge events. ACKNOWLEDGMENTS This paper has been authored by employees of UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy. Accordingly, the United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Simulated Capacity for 200 Parcels and 8 Parcels/Pallet

1800

2000

1600 1500 Capacity

1400 1000

1200 1000

500

800 0 100%ETD

600

75%ETD/25%EDS

300

50%ETD/50%EDS 180

25%ETD/75%EDS ETD/EDS Combination

400

240 100%EDS

200

REFERENCES [1] S. Robinson, Simulation: The practice of model development and use, Wiley, 2004. [2] Cincinnati/Northern Kentucky International Airport, Annual Report, 2007, (Available at http://www.cvgairport.com/files/files/CVG%202007.pdf). [3] Allgood G., Olama M.M., Rose T., and Brumback D., 2009, “Aviation Security Cargo Inspection Queuing Simulation Model for Material Flow and Accountability”, Proceedings of the SPIE Defense, Security and Sensing Conference, vol. 7305, no. 18, 12 pages. [4] http://www.mathworks.com/products/simevents/. [5] http://www.mathworks.com/.

120 Cargo Closeout Time

Figure 14. System’s potential capacity as a function of inspection modality and cargo closeout time.

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