Virginia's Department of Motor Vehicles (DMV) serves a customer base of approximately 5.6 million licensed drivers and ID card holders and 7 million registered vehicle owners. DMV has more daily face-to-face contact with Virginia's citizens than any other state agency [1]. The DMV faces a major difficulty in keeping up with the excessively large customers' arrival rate. The consequences are queues building up, stretching out to the entrance doors (and sometimes even outside) and customers complaining. While the DMV state employees are trying to serve at their fastest pace, the remarkably large queues indicate that there is a serious problem that the DMV faces in its services, which must be dealt with rapidly. Simulation is considered as one of the best tools for evaluating and improving complex systems. In this paper, we use it to model one of the DMV centers located in Norfolk, VA. The simulation model is modeled in Arena 10.0 from Rockwell systems. The data used is collected from experts of the DMV Virginia headquarter located in Richmond. The model created was verified and validated. The intent of this study is to identify key problems causing the delays at the DMV centers and suggest possible solutions to minimize the customers' waiting time. In addition, two tentative hypotheses aiming to improve the model's design are tested and validated

Arnaout, Georges M.

Bowling, Shannon

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295
Improving Customer Waiting Time at a DMV Center Using
Discrete-Event Simulation
Georges M. Arnaout (Ph.D. Student)*; Shannon Bowling (Ph.D.)
Engineering Management and Systems Engineering - Old Dominion University (Norfolk, VA)
garna001@odu.edu
Abstract. Virginia's Department of Motor Vehicles (DMV) serves a customer base of approximately 5.6 million
licensed drivers and ID card holders and 7 million registered vehicle owners. DMV has more daily face-to-face
contact with Virginia's citizens than any other state agency [1]. The DMV faces a major difficulty in keeping up
with the excessively large customers' arrival rate. The consequences are queues building up, stretching out to
the entrance doors (and sometimes even outside) and customers complaining. While the DMV state
employees are trying to serve at their fastest pace, the remarkably large queues indicate that there is a serious
problem that the DMV faces in its services, which must be dealt with rapidly. Simulation is considered as one
of the best tools for evaluating and improving complex systems. In this paper, we use it to model one of the
DMV centers located in Norfolk, VA. The simulation model is modeled in Arena 10.0 from Rockwell systems.
The data used is collected from experts of the DMV Virginia headquarter located in Richmond. The model
created was verified and validated. The intent of this study is to identify key problems causing the delays at
the DMV centers and suggest possible solutions to minimize the customers' waiting time. In addition, two
tentative hypotheses aiming to improve the model's design are tested and validated.
1. INTRODUCTION
The usage of simulation has increased noticeably
in the recent years due to the advancement of
computer technology. The act of simulating
behaviors and situations has been adopted in
multiple areas like military, social behavior, flight
simulators, robotics, etc. In this paper, we use
Discrete Event Simulation (DES) since our aim is
modeling the DMV system as it progresses over
discrete times in a non-continuous fashion. In this
model, we attempt to mimic the behavior of the
real DMV center system by building our model
from variables that are generated from data that is
collected from experts of the DMV headquarter in
Richmond, VA. The model is examined thoroughly
and conclusions and solutions are produced from
this study. Additionally, the study identifies two
possible scenarios to enhance the system, and
determines if they present a statistical significance
to the model.
1.1 OBJECTIVES OF THE STUDY
The study is conducted in order to (1) minimize the
customer waiting time at the DMV - 850 Widgeon
Road, Norfolk, (2) give insights towards minimizing
the customer waiting time at all the DMV centers,
statewide, or around the country, (3) attempt to
improve the existing model (i.e. Should we add
another check-in window?), and (4) give
suggestions aiming for optimizing the system. The
focus of the study will be on reducing the following
three delays: 1-The ticketing waiting time: time
needed to obtain a service ticket. 2-The service
window waiting time: time needed to reach the
service window and be serviced. 3-The
transaction time: time needed to be serviced.
2.0 THE MODEL
The model built using Arena (version 10) from
Rockwell Systems, is a miniature of the existing
DMV center located at 850 Widgeon Rd, Norfolk,
VA.
2.1 Model Details
Customers arrive in a stochastic way according to
an inter-arrival rate produced by exhaustive
observations conducted at the Widgeon center.
The model has a main queue called the "check-in"
https://ntrs.nasa.gov/search.jsp?R=20100012863 2019-08-29T19:05:59+00:00Z
296
queue. This is the main queue where all customers
have to pass through in order to get their tickets
and proceed to the nearby seating area, waiting for
their ticket number to be announced. When a
customer's ticket number is announced, the
customer proceeds to one of the service windows
in order to be served in a FIFO manner. As there
are 14 service windows, according to our extensive
observations, only 10 windows are being used at
the same time. Thus, for the sake of the study, we
consider 10 windows with 10 servers serving on
them. There is a separate M/M/1 queue for each of
the windows (i.e. 10 separate queues) that we will
emphasize graphically in our model. In the real
system, we cannot observe these queues
separately as the customers are seated all
together. There is a range of 10 to 14 servers
serving at these windows according to a 'real'
weekly schedule obtained from the Widgeon DMV
center. The waiting times, transaction times, and
other delays are generated from the collected data
(please refer to the Data Collection section). The
model runs for 8 hours on weekdays (serving from
gam until 5pm) and 4 hours on Saturday (8am until
12pm). Each DMV agent is allowed to take one '30
minutes break' during the day on weekdays, and
no breaks on Saturdays. The breaks are divided
between three groups. First group of employees
can take the break between 11 am and 2pm. The
second group can take the break between 1pm
and 2pm. Finally, the third group of employees can
take the break between 2pm and 3pm. As a
summary, the model works like the following:
Customers arrive to the system, wait at the check
in (ticketing) queue in order to obtain a ticket.
Then, customers go wait in line in order to obtain a
ticket (ticketing wait time) before moving to one of
the 10 available service windows. The customers
wait for another delay until they are serviced by a
DMV agent, which is the service wait time. Then,
the customers are serviced with a service delay
called Transaction Time. Finally, the customers
leave the system.
2.2 Model Constraints
Our study has several limitations due to the time
factor and the nature of the study:
• The types of services that customers request
are ignored. Due to the limitation of the data
collected, the types of services are overlooked
and all the service delays are recorded as one
service type delay. For example, the time that a
customer spends for obtaining new car license
plates is combined with the time of another
customer trying to obtain a driving license
(which is remarkably longer).
• Customers that leave the DMV center for any
reasons (e.g. missing documents) are still
counted in the time statistics but not modeled in
our system.
• The customer's inter-arrival rate and the waiting
time at the main ticketing queue are obtained by
interviewing experts as well as extensive
observation at the DMV Widgeon center [3].
• Holidays as well as the busiest days of the
month (i.e. first day and last day of the month)
are not counted in our model. However, Fridays
and especially Saturdays are considered busier
than the other days.
• The DMV employees' weekly schedule is
considered static although it changes weekly.
• We assume that customers arriye one at a time
to the DMV center.
• All the units used in this study are in minutes.
2.3 Model Design
The model built using Arena 10.0 is represented in
figure1. The key model variables are:
• 10 service windows
• 10 Service Queues - (Waiting Time)
• Customers arriving in a stochastic way to the
center
• Service Time (or Trans Time)
• Main queue - customer check in (time needed
to acquire a ticket)
• Number of DMV agents defined by a schedule
ranging between 10-14 during the day
includin breaks
Figure 1: DMV Model Design in Arena 10.0
A: Entity creating the users arriving to the DMV in
a stochastic way.
B: Recorder that catches the time of arrival of the
customers for further calculation
c: Module that seizes the agent on the main check
in queue in order to serve the customer
297
0: Module that delays the service time of the agent
according to a distribution explained in the Data
Collection section
E: Module that releases the agent after finishing
from servicing the customer at the check in queue
F: Module that delays the customer service time
before reaching the service window according to
the next available window queue (refer to the Data
Collection section).
Table 1: DMV Weekly Schedule
26-.1.., 27..lan :za.-Jan ~.., 3Chlan 31.Jan
MON InES WED TlfJRS FRI SAT
0 Rose 8:QO.5:30 8:()()·6:3O sdo 8:Q0.6:3O 8,00-5:30 8:00-1:00
c Btanclll 8:~:OO 9:1s.6:OO 9:1s.6:OO 9:1s.6:00 9:1s.6:OO .do
0 DeIn 8:Q0.6:30 8:()().6:3O 8:()().6:3O ldo 8:Q0.6:3O 7:JO.l2:30
c UIIIe 8:~:OO 8:J0.6:OO 8:J0.6:OO 8:J0.6:OO sdo 8:00-1:00
Ancna 8:QO.S:.S 8:JO.5:~ :30-5:~ .do 8:30-5:45 7:45012:.5
AcMnda 8:()().5:~ sdo 8:JO.S:~ 8:JO.5:45 8.30-5:~ 17:45-12:.5
Brion 8:Q0.6:~ 8:JO.S:~ sdo 8:30-5:45 8:30-5:~ 1:45-12:~
Ic.oiY" 8:QO.S:~ sdo 8:30-5:45 8:30-5:45 8:30-5:45 1:45-12:45
GIo ado 8:JO.5:~ 18:30-5:45 8:JO.S:~ 8'30-5'~ 7:4$012:45
J..... sdo 8:JO.S:~ 8:30-5:~ 8:30-5:45 8:30-5:45 7'45-12:.5
MIIVlbt 8·QO.5:45 8:30-5:~ :JO.S:45 sdo 8:30-5'~ 7:4$012:45
M"",iN 8:QO.5:~ 8:JO.5:~ 8:30-5:45 8:JO.5:~ ado 1.45-12:45
au-t AT T tIPTON 8:45-5:30 8.~.3O sdo
S"_n 8:QO.S:45 :~:JO 8:~:3O 18:~:JO 8:45-5:30 sdo
Theresa 8:00-5:45 '30-5:45 8:30-5:~ 8:JO.S:~ 8:30-5:~ N$ol2:45
Shlntv 8:QO.S:~ TRHG TRHG TRHG sdo 7.45-12:~
P-l.'.
T.....kI 11:Q0.4:OO 8:Q0.4:00 8:Q0.4:00 11:(1).4:00 sdo 7:45-12:~
G: Module that decides which is the next available
service window according to a small program to
calculate the service window containing the least
number of customers waiting in its queue.
H: Ten service windows that serve the customers.
I: Recorder that calculates the flow time of the
customers in the system using the previous
recorder (8).
J: Module that allows the customers to exit and
leave the system.
3. OATA COLLECTION
The data is mainly collected from extensive
observation of the center, interviewing experts from
the headquarters, and from the DMV weekly data
sheets provided by the DMV experts [2]. The
weekly data collected from the DMV experts
provided with around 100 data points that were
used to generate the distributions of the related
delays. The customers inter-arrival rate is
generated from a schedule that resulted from
extensive observation and interviewing the
system's experts. The arrival schedule is
implemented from Monday through Saturday. On
weekdays, the customers arrive between 9am and
5pm. On Saturdays, the customers arrive between
8am and 12:00pm. The ticketing waiting time is
generated from a Triangular distribution that
resulted from extensive observation of the center
and the behavior of customers. The expression
used is EXPO (0.62). The service waiting time
delay is generated from a Weibull distribution that
resulted from plotting the data points on a
histogram (refer to Figure 2), provided by the DMV
weekly data. The expression produced is: 4 +
WEIB (17, 2). We considered this distribution a
good fit for our collected data because it has a very
low Square Error (Which is 0.005248) and the p-
value is remarkably larger than 0.05.
~leW:lIl
~t... 4. If••
__~ L.i:4I
:w~rer.
r1t_•• 5
1ttUU .. r:- .,
,_,.~ -LM
~~"""LJ"
- ...rut,.~ ...a
(xu_--.,..,ua) .. u
Figure 2: Histogram of Service Waiting Time Delay
The transaction time (service time) is generated
from a log normal distribution that resulted from
plotting the data points provided by the DMV
weekly data in the histogram shown in Figure 3.
The expression produced is: 5.2 + LOGN (1.81,
0.803). Figure 3 plots the histogram of the data
collected and includes the distribution summary.
We consider this distribution a good fit for our
collected data because it has a very low Square
Error (which is 0.0628) and the p-value: 0.101 is
remarkably larger than 0.05.
.~tc.-..l
!lI;tallWo: ~, •••• "
.... t::a:t....14
"'--..~ 01hpuiltU:MCa ·1
~~..ICUu: .Ut
~,....(...
_...
~t~ ,"UI
(~J-*'''.
Figure 3: Histogram of the Transaction Time Delay
298
4. OUTPUT ANALYSIS
After running the model for 10 replications where
each replication represents a week composed of 6
business days, we came up with the following
results:
• The weekly average of customers coming to the
DMV center of Widgeon Road is 2054
customers.
• The total average waiting time for each
customer (or customer flow time) is 41.65
minutes.
The queuing delays are represented in details in
Table 2 below:
Table 2: DMV Model Statistical Results_Tn.
- --
- -
.
selz 8 for t"etlan Queue 31771 1 63 , 5112 7 9SlA
SeNles WindOW' 1 Queue 23011 012 10126 • 3661
ServIce WIndow 10 Oueue 29 QO.,
"'
113725 492350
ServIce Window 2 Oueue 157113 416 10663 211253
Service Window 3 Oueue 184753 505 80317 3" 3310
Service Window" Queue 209412 5 '2 9 '003 368350
Service Window 5 Queue 230120 609 9141' '08050
Service Window 6 OUeue 250501 660 94656 u 1914
ServIce Window 7 Queue 263464 690 97793 462830
Service Window 8 Queue 21 2823 681 103778 462231
Service Wmdow 9 Queue 28 ....66 699 109392 t7 6204
We observe that there is an average of 3.77
minutes wait time at the check-in queue (ticketing)
which we categorized as fair. In addition, looking at
the queuing occurring at the service windows, the
average transaction time was around 21.65
minutes. Here, it is necessary to mention that the
service window delays are not the delays only
related to the service time at the window, but also
includes the waiting time before the customers get
served by an agent. The average service time is
around 8 min which in our opinion does not need
further improvement.
That leaves the deficiency of the system to only
one variable which is the excessive arrival rate of
the customers, which in turn, affects all the other
delays causing the excessive queues.
5. MODEL VERIFICATION AND VALIDATION
Our V&V was conducted in parallel with the
system's experts at the DMV headquarters. By
comparing our model's results with their weekly
data, we've found that our generated distributions,
our model (with its variables), and our results were
valid. For the inter-arrival rate of the customers,
according to the DMV weekly sheets (of the real
system), an average of 2105 customers visited the
DMV at Widgeon weekly. According to our model,
the average was 2054 customers which is
considered very close, and therefore valid. As for
the other delays (e.g. Transaction delay), the
distributions were verified via emails with a senior
analyst at the DMV headquarters in Richmond
[reference needed]. The final results of the study
and the possible solutions were submitted to the
headquarters upon their request and are being
studied by their analysts.
6. ALTERNATIVE SCENARIOS
Observing the customers' waiting time at the
ticketing window in our model's animation, in Table
2, and in the real system, inspired us to come up
with two different alternatives different from having
one main ticketing queue.
6.1 Alternative 1
We considered having an additional ticketing
window resulting in two parallel check- in queues
that are served by two agents. After implementing
this addition to our model and running it for 10
replications Uust like the original model), we
realized that the customer waiting time was slightly
reduced from 41.65 minutes to 40.28 minutes. In
order to find out if this alternative was worth
implementing, we conducted a Paired-t test on the
two approaches (this one and the original model).
We concluded that the change was not statistically
significant (the two means overlapped at 0.05
level).
6.2 Alternative 2
The second alternative was to increase the number
of agents by having 14 agents working at all times
(including breaks). This alternative has two sub-
alternatives. One, by increasing the number of
agents to the original model without adding another
ticketing queue (i.e. having two ticketing windows),
and the other one by adding another ticketing
window. After implementing the changes (in both
SUb-alternatives) and running the model for 10
replications, we concluded that this scenario is also
not statistically significant.
7. CONCLUSION
At the beginning of the study, we were considering
the proposal of a Self-Check in kiosk to speed up
the ticketing phase, as a parallel approach to the
main check in service window. Our reason for
proposing such an approach was our belief (prior
to running the model) that having two separate (but
parallel) check-in queues will speed things up and
minimize the customers' waiting time. After
299
implementing and running the model, and after
experimenting with the two alternatives proposed
(refer to section 6), we concluded that having a
self-check in kiosk will not have a significant
positive difference on the existing model, and
therefore decided to drop that suggestion. Thus in
our opinion, this limits the delay to two main
system design gaps. Either the service time
(transaction time) is relatively high, or the arrival
rate is just too excessive. For the first gap, the
service time can be reduced by increasing the
number of agents but also, increasing the number
of service windows proportionally. This would
reduce the service time remarkably and affect the
overall waiting time of the visiting customers.
Here, it must be noted that the pace of the service
is relatively fine. The serving pace does not need
to be enhanced since according to our records, the
average transaction time for each customer is
21.65 minutes (including the time waiting to be
serviced), which is relatively fair. Therefore, the
queuing is not occurring from the transaction time
(Le. service time). As for the second gap, the
arrival rate can only be reduced by offering more
online services (but also keeping the option of
physically visiting the DMV center for these
services). This will reduce the arrival rate of
customers since, with the digital age and the ease
of access to go online, customers would most
probably prefer conducting the transactions online
(e.g. from their work office) rather than spending
time to visiting the DMV center. Several DMV
centers started giving appointments to their
customers in order to balance and control their
inter-arrival rate. This approach is also feasible and
could be implemented at the Widgeon center.
REFERENCES
1. DMV. (2009). Retrieved April 25, 2009, from
www.dmv.state.va.us/webdoc/about/index.asp
2. Powell, Minni, personal communication, June
2009
3. White, Robert, personal communication, July
15,2009


Improving Customer Waiting Time at a DMV Center Using Discrete-Event Simulation

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