Objective
The objective of this simulation analysis is to operate the security checkpoint in the most effective manner. The most
effective manner is defined as operation with the minimum average individual time in the system with the minimum
number of personnel while maintaining the current level of security. Though the checkpoint operates throughout the
day, effective operation is critical during periods of high arrival and departure activity. These periods are a result of
the hub-and-spoke system used by the major airline in the airport involved in this study. These periods of high
activity dictate the use of a terminating system simulation approach. Given the objective of this analysis, the
following questions need to be addressed:
1. For a given number of security personnel, which configuration will result in the minimum average
customer service time?
2. If the checkpoint experiences an equipment malfunction, how should the personnel be reassigned to
result in minimum average customer system time?
Data Collection and Fitting
A total of eight data distributions were collected and fitted. These included the interarrival times of customer
batches, the customer arrival batch size, ticket-checking service time, travel time from the ticket checker to the metal
detector, whether or not the customer has luggage, the x-ray service time, the metal detector service time, and the
customer metal detector failure rate. The followings are the definitions of each data distribution collected.
1. Interarrival Times of Customers
The interarrival times of customers were obtained by observing the interval between the arrival of one batch of
customers and the arrival of the next batch of customers. Arrival was defined as either the physical location within
arm’s length distance of the ticket checker or cessation of forward movement upon reaching the end of the ticketchecking
queue.
2. Customer Arrival Batch Size
A batch of customers consisted of one or more individuals who arrived at the first point of the system
simultaneously. Batch sizes larger than one typically occurred with husband and wife passengers, families of
passengers, groups of businessmen, and air crew. The size of the arrival batch was recorded with the batch
interarrival time.
3. Ticket-Checking Service Time
This period begins with the arrival of the customer within arm’s reach of the ticket checker. The service time
consists of checking either the ticket or identification of the customer for validity. The ticket-checking service time
ends when the customer begins forward movement and allows the ticket checker to service the next customer.4. The Presence of Luggage
The presence of luggage was defined as any object that the customer was required to have x-rayed. This included
luggage, boxes, and radios.
5. Travel Time
This is defined as the time required after the end of the ticket-checking time and before arrival at either the x-ray
machine, the metal detector, or the queue of either.
6. X-Ray Service Time
X-ray service time begins with the customer first placing objects on the end of the x-ray machine. The service time
includes the time required by the x-ray operator to inspect the objects and ends when the individual removes the
luggage. Data for this specific service time were recorded so that they were not dependent on the time the passenger
was being processed through the metal detector. Thus, these data reflect the system time that can be attributed to the
presence of baggage.
7. Metal Detector Service Time
The collection of metal detector service times depended on whether the customer possessed luggage. In the case of
no luggage, metal detector service time began with passenger passing the front of the x-ray machine. In the case of
the presence of luggage, the time began when the passenger released the luggage onto the front of the x-ray machine
and began forward movement toward the metal detector. Service time includes the period during which the customer
has exclusive control over the metal detector and operator. Time includes the customer passing through the metal
detector. Time ends when either the customer is not stopped by the operator and physically passes the operator or
fails the inspection and passes backwards through the metal detector. Observations of customers who failed the
metal detector indicated that other customers passed through the metal detector while the failed customer searched
for possible causes of failure.
8. Customer Metal Detection Failure
This occurred when the customer failed to remove suspect metal objects from his/her person. These data were
collected on the basis of the number of customers who failed out of the total number of customers observed.
Data Fitting
This section consists of fitting the observed data to theoretical probability distributions. Distribution fitting was
conducted with the chi-square and Kolmogorov–Smirnov goodness of fit tests.
1. Interarrival Times of Customer Batches to the Checkpoint
This data were fitted to a lognormal distribution with a mean of 3.14 and a standard deviation of 3.07.
2. Customer Arrival Batch Size
These data were fit to a geometric distribution. In this case, the empirical distribution was modified by subtracting
one from each observation. Thus, the batch size minus one was fit to a geometric distribution with p = 0.552695.
3. Ticket-Checking Service Time
These data were fit to a gamma distribution with an value = 1.91 and a value of 0.99.
4. Travel Time
Travel time to the x-ray/metal detector group I was fit for coming from a uniform distribution [0.8, 2.2] thousandths
of hours. Travel time to the x-ray/metal detector group 2 was fit for coming from a uniform distribution [1.1, 3.2]
thousandths of hours.
5. The Presence of Luggage
These data consisted of the observation that, of the customers observed, 10% did not possess objects that required
being x-rayed. This is a Bernoulli trial with only one of two outcomes: the customer has objects or the customer
does not.
6. X-Ray Service Time
These data were missing, so you will need to assume this distribution. Before you make any assumption, you should
do some research to find the information to support your assumption. You may do some research on internet like
Google. Please state your assumption and the reason why this assumption is suitable for this data.
7. Metal Detector Service Time
These data were missing, so you will need to assume this distribution. Before you make any assumption, you should
do some research to find the information to support your assumption. You may do some research on internet like
Google. Please state your assumption and the reason why this assumption is suitable for this data.
8. Customer Metal Inspection Failure
These data consisted of the observation that 3% of the customers observed failed the inspection. This is
a Bernoulli trial with one of two possible outcomes: the customer fails the inspection, or the customer
does not.Assumptions
A total of eleven assumptions were incorporated in the modeling of the security checkpoint system. The first
assumption is that passengers cannot balk. This means that in order to obtain access to the loading gate area, all
customers must proceed through the security checkpoint. The second assumption is that passengers do not jockey.
Jockeying is jumping between queues if another queue becomes shorter in length. Actual observation indicated that
90% of the customers who possessed luggage appeared unwilling to jockey between queues.
Queue Selection
When presented with more than one queue, it is assumed that the customer will first select the shortest queue. If
queues exist of similar length, it is assumed that the customer will select the nearest queue.
Size of Queue
It is assumed that the security personnel will not shut down the system regardless of how long a system queue
becomes. In the case of the ticket-checking queue, the queue extends down the main concourse hall. When the x-ray
queue exceeded a small number of customers, the ticket checker himself moved down the main concourse hall.
Customers Who Fail the Metal Detector Go to the End
It is assumed that customers who fail the metal detector inspection go to the end of the queue. Observation of this
situation indicated that while the failed customer attempted to determine the cause of failure, other customers in the
queue bypassed the failed customer.
Service Rates Are the Same among Checkers and Operators
It is assumed that the service rate between ticket checkers and machine operator is the same for a given type of
operation. This is a simplifying assumption.
Service Rate Is Independent of Queue Size
It is assumed that thorough inspection is paramount and that the number of customers waiting in a queue will not
result in a ticket checker or machine operator changing work method so that the customer system time is reduced.
Customers Do Not Leave without Their Luggage
It is assumed that customers will not leave the system without their luggage.
No Restricted Items Are Discovered
The discovery of a restricted item such as a firearm or explosive device is not a regular occurrence. Thus, this
situation is outside the scope of this model. In order to model this event correctly, knowledge and observation of
such an incident would be necessary.
No Breaks
It is assumed that, during the period of concern, all security operators will remain on duty without taking breaks.
First In–First Out
It is assumed that the operation of all queues is performed in a first in–first out manner.
Generation of Customers
The generation of customers included the creation according to interarrival times and batch sizes. As each customer
is created, ticket service time and whether or not the customer has luggage are first determined. If the customer does
not have baggage, the customer’s metal detector service time is generated. If the customer has luggage, then both an
x-ray and metal detector time is determined.
Starting Condit
Objective
The objective of this simulation analysis is to operate the security checkpoint in the most effective manner. The most
effective manner is defined as operation with the minimum average individual time in the system with the minimum
number of personnel while maintaining the current level of security. Though the checkpoint operates throughout the
day, effective operation is critical during periods of high arrival and departure activity. These periods are a result of
the hub-and-spoke system used by the major airline in the airport involved in this study. These periods of high
activity dictate the use of a terminating system simulation approach. Given the objective of this analysis, the
following questions need to be addressed:
1. For a given number of security personnel, which configuration will result in the minimum average
customer service time?
2. If the checkpoint experiences an equipment malfunction, how should the personnel be reassigned to
result in minimum average customer system time?
Data Collection and Fitting
A total of eight data distributions were collected and fitted. These included the interarrival times of customer
batches, the customer arrival batch size, ticket-checking service time, travel time from the ticket checker to the metal
detector, whether or not the customer has luggage, the x-ray service time, the metal detector service time, and the
customer metal detector failure rate. The followings are the definitions of each data distribution collected.
1. Interarrival Times of Customers
The interarrival times of customers were obtained by observing the interval between the arrival of one batch of
customers and the arrival of the next batch of customers. Arrival was defined as either the physical location within
arm’s length distance of the ticket checker or cessation of forward movement upon reaching the end of the ticketchecking
queue.
2. Customer Arrival Batch Size
A batch of customers consisted of one or more individuals who arrived at the first point of the system
simultaneously. Batch sizes larger than one typically occurred with husband and wife passengers, families of
passengers, groups of businessmen, and air crew. The size of the arrival batch was recorded with the batch
interarrival time.
3. Ticket-Checking Service Time
This period begins with the arrival of the customer within arm’s reach of the ticket checker. The service time
consists of checking either the ticket or identification of the customer for validity. The ticket-checking service time
ends when the customer begins forward movement and allows the ticket checker to service the next customer.4. The Presence of Luggage
The presence of luggage was defined as any object that the customer was required to have x-rayed. This included
luggage, boxes, and radios.
5. Travel Time
This is defined as the time required after the end of the ticket-checking time and before arrival at either the x-ray
machine, the metal detector, or the queue of either.
6. X-Ray Service Time
X-ray service time begins with the customer first placing objects on the end of the x-ray machine. The service time
includes the time required by the x-ray operator to inspect the objects and ends when the individual removes the
luggage. Data for this specific service time were recorded so that they were not dependent on the time the passenger
was being processed through the metal detector. Thus, these data reflect the system time that can be attributed to the
presence of baggage.
7. Metal Detector Service Time
The collection of metal detector service times depended on whether the customer possessed luggage. In the case of
no luggage, metal detector service time began with passenger passing the front of the x-ray machine. In the case of
the presence of luggage, the time began when the passenger released the luggage onto the front of the x-ray machine
and began forward movement toward the metal detector. Service time includes the period during which the customer
has exclusive control over the metal detector and operator. Time includes the customer passing through the metal
detector. Time ends when either the customer is not stopped by the operator and physically passes the operator or
fails the inspection and passes backwards through the metal detector. Observations of customers who failed the
metal detector indicated that other customers passed through the metal detector while the failed customer searched
for possible causes of failure.
8. Customer Metal Detection Failure
This occurred when the customer failed to remove suspect metal objects from his/her person. These data were
collected on the basis of the number of customers who failed out of the total number of customers observed.
Data Fitting
This section consists of fitting the observed data to theoretical probability distributions. Distribution fitting was
conducted with the chi-square and Kolmogorov–Smirnov goodness of fit tests.
1. Interarrival Times of Customer Batches to the Checkpoint
This data were fitted to a lognormal distribution with a mean of 3.14 and a standard deviation of 3.07.
2. Customer Arrival Batch Size
These data were fit to a geometric distribution. In this case, the empirical distribution was modified by subtracting
one from each observation. Thus, the batch size minus one was fit to a geometric distribution with p = 0.552695.
3. Ticket-Checking Service Time
These data were fit to a gamma distribution with an value = 1.91 and a value of 0.99.
4. Travel Time
Travel time to the x-ray/metal detector group I was fit for coming from a uniform distribution [0.8, 2.2] thousandths
of hours. Travel time to the x-ray/metal detector group 2 was fit for coming from a uniform distribution [1.1, 3.2]
thousandths of hours.
5. The Presence of Luggage
These data consisted of the observation that, of the customers observed, 10% did not possess objects that required
being x-rayed. This is a Bernoulli trial with only one of two outcomes: the customer has objects or the customer
does not.
6. X-Ray Service Time
These data were missing, so you will need to assume this distribution. Before you make any assumption, you should
do some research to find the information to support your assumption. You may do some research on internet like
Google. Please state your assumption and the reason why this assumption is suitable for this data.
7. Metal Detector Service Time
These data were missing, so you will need to assume this distribution. Before you make any assumption, you should
do some research to find the information to support your assumption. You may do some research on internet like
Google. Please state your assumption and the reason why this assumption is suitable for this data.
8. Customer Metal Inspection Failure
These data consisted of the observation that 3% of the customers observed failed the inspection. This is
a Bernoulli trial with one of two possible outcomes: the customer fails the inspection, or the customer
does not.Assumptions
A total of eleven assumptions were incorporated in the modeling of the security checkpoint system. The first
assumption is that passengers cannot balk. This means that in order to obtain access to the loading gate area, all
customers must proceed through the security checkpoint. The second assumption is that passengers do not jockey.
Jockeying is jumping between queues if another queue becomes shorter in length. Actual observation indicated that
90% of the customers who possessed luggage appeared unwilling to jockey between queues.
Queue Selection
When presented with more than one queue, it is assumed that the customer will first select the shortest queue. If
queues exist of similar length, it is assumed that the customer will select the nearest queue.
Size of Queue
It is assumed that the security personnel will not shut down the system regardless of how long a system queue
becomes. In the case of the ticket-checking queue, the queue extends down the main concourse hall. When the x-ray
queue exceeded a small number of customers, the ticket checker himself moved down the main concourse hall.
Customers Who Fail the Metal Detector Go to the End
It is assumed that customers who fail the metal detector inspection go to the end of the queue. Observation of this
situation indicated that while the failed customer attempted to determine the cause of failure, other customers in the
queue bypassed the failed customer.
Service Rates Are the Same among Checkers and Operators
It is assumed that the service rate between ticket checkers and machine operator is the same for a given type of
operation. This is a simplifying assumption.
Service Rate Is Independent of Queue Size
It is assumed that thorough inspection is paramount and that the number of customers waiting in a queue will not
result in a ticket checker or machine operator changing work method so that the customer system time is reduced.
Customers Do Not Leave without Their Luggage
It is assumed that customers will not leave the system without their luggage.
No Restricted Items Are Discovered
The discovery of a restricted item such as a firearm or explosive device is not a regular occurrence. Thus, this
situation is outside the scope of this model. In order to model this event correctly, knowledge and observation of
such an incident would be necessary.
No Breaks
It is assumed that, during the period of concern, all security operators will remain on duty without taking breaks.
First In–First Out
It is assumed that the operation of all queues is performed in a first in–first out manner.
Generation of Customers
The generation of customers included the creation according to interarrival times and batch sizes. As each customer
is created, ticket service time and whether or not the customer has luggage are first determined. If the customer does
not have baggage, the customer’s metal detector service time is generated. If the customer has luggage, then both an
x-ray and metal detector time is determined.
Starting Condit
การแปล กรุณารอสักครู่..