Analyzing The University Canteen Performance A Case Study

Proceedings of the 11th
Symposium onApplied Science, Business & Industrial Research – 2019
ISSN 2279-1558, ISBN 978-955-7442-27-3
122
Analyzing the University Canteen Performance: A Case Study
ABSTRACT
The university canteen is a queuing system with time dependent arrival patterns and
limited resources. It has created the problem of students waiting in the queue during the lunch
hour. This study purposively selected a Sri Lankan state university canteen composed of two
counters and corresponding queues. The data were collected during the lunch hour of five
consecutive week days. The sample included 200 students. The queuing system was modeled using
the student version of Rockwell ARENA 14.5. The inter arrival times were calculated and inserted
to the input analyzer to find the corresponding arrival patterns of students to the university canteen
queues. It showed non uniform arrival rates explained by different patterns. The modeled system
was run for one hour to find average values for waiting time of the students separately in two
queues, number waiting in queues and number of arrivals and service receipts. The simulation
showed that 64 students received the service among 64 successive arrivals. The results explained the
average waiting times in queue 1 and queue 2 to be 2.35 and 3.03 in minutes respectively. The
number waiting at counter 1 and 2 were 1.26 and 1.62 customers. The study further revealed the
possibility of reducing waiting times at counter 1 and counter 2 to 0.68 and 0.89 minutes
respectively by doubling the resources at both counters. The findings suggested improving the
customer service of the system by opening an additional counter during the lunch hour and
redesigning the layout considering the financial feasibility.
KEYWORDS: Multi-server, Queuing system, Rockwell ARENA, Simulation, University Canteen
1 INTRODUCTION
Queues also called as waiting lines must
be an experience of every individual in
performing their daily tasks. Queues are a
general phenomenon in supermarkets,
hospitals, restaurants, road networks and
many other places. Although waiting in lines
is a waste of time, it would not be
disciplinary to avoid them in getting a
service done. Thus everyone has to bear the
frustration associated with waiting.
The general scenario of a waiting line
occurs when the service rate of a particular
server is less than the arrival rate of entities
to be served by the system. Thus entities (or
customers) have to wait till their chance is
received. The pattern of arrivals to a
queuing system may be random, the
population of customers may be finite or
infinite, and the number of queues and
number of servers may be single or multiple.
Also many queue disciplines can be possible
in a system.
This study was related with the most
common waiting lines in university
canteens. Hostel canteen of a state university
in Sri Lanka was chosen for the study. The
referred faculty possessed only one hostel
canteen Therefore, long queues could be
visible almost every time of the day. The
system had only two counters. Thus two
waiting lines could be seen. Students
selected the queue randomly. The
population size was finite where only the
university students, academic and non-
academic staff and others inside the
university were permitted to enter the
canteen, the waiting room size was finite
and queue discipline was First In First Out
basis. The pattern of arrivals and service
Rambandara RDSS, Weerakoon WMTNK, Dilanthi MGS
Department of Industrial Management, Wayamba University of Sri Lanka
rdssrambandara@gmail.com

Rambandara, Weerakoon & Dilanthi
123
provision were assumed to be Poisson
Accordingly, the Kendall‟s notation for, the
model was (M/M/2): (FIFO/L/∞).
The study simulated the system using
the student version of Rockwell Arena 14.5.
The objectives of the study were to analyze
the current system using Arena software and
to calculate the average values of, the
number of customers entered into the system
in a given length of replication, the number
of customers served by counters in a given
period of time, the customer waiting time in
queues and the number of customers waiting
in the queues.
2 LITERATURE REVIEW
Simulation of service processes is a
research topic seldom studied by researchers
around the world.
Nsude et al. (2017) have presented the
underlying mathematical concepts of queue
models: arrival and service time
distributions, queue disciplines and queue
behavior in a multiple-lines, multiple server
queuing system. The operating characteristic
formulas for multiple-server queuing model
meant to evaluate performance of practical
queuing systems were also presented.
Different ratios of average arrival time and
service times have been obtained to
determine the optimal number of service
facilities (servers) appropriate for the
system. The authors have suggested a
reduction in the number of servers in the
system from four to three to reduce the idle
time of the servers and also to reduce the
operation cost.
Aqil (2016) has simulated a local
cafeteria system using student version of
Arena. The study focused on how much
baristas were needed for proper management
of the selected cafeteria as well as how
much sitting tables or chairs were needed for
the proper flow of customers. After
analyzing the existing system, the author has
decided to fully utilize all the resources and
to add two tables for couples and one for
groups. Thereby, a new properly managed
cafeteria has been designed through
reduction of the total calculated cost. Further
the author recommends applying the concept
for various other service industries where
customer behavior is of core importance.
Sanjay et al. (2014) has presented the
salient aspects of a discrete event simulation
study carried out on a student canteen for
performance improvement. The advantage
of adding an extra server has been proved in
the study both statistically and through
simulation results. Finally, changing the
method of taking coupon and changing the
position of the menu card have been
recommended for further studies.
In their study, Dharmawirya et al. (2012)
have observed the actual waiting time of
customers for a number of fast food
restaurants, and have compared the metrics
with waiting times that customers expected.
During lunch time peak hours, customers
have spent an average 5.4 minutes waiting
before they could get their orders. This total
time consisted of 2.42 minutes of queuing
time and 2.98 minutes of service time. In
addition, they have surveyed 51 respondents
asking them to give the three most important
factors in choosing fast food restaurants. Out
of the given options, speed, menu variation,
price, friendliness, cleanliness, atmosphere,
and promotional items or discounts, the top
three factors that have been selected by the
respondents were speed, price and
cleanliness.
Chou & Liu (2008) built a simulation
model to study the queuing system in a fast-
food restaurant in Taiwan. From the results
of the study it has been concluded that the
manager of the restaurant should recruit

ANALYZING THE UNIVERSITY CANTEEN PERFORMANCE: A CASE STUDY
124
another server during peak hours so that the
customers' waiting time can be reduced.
Curtin et al. (2005) has noted higher
service times at a busy fast food restaurant
in a campus. Thus they have carried out
standard simulation study steps, modeled
and evaluated several scenarios based on
customer system time. It has been observed
that the utilization of the cash registers is
high. Consequently, it is recommended to
operate five servers to reduce customer
waiting time and to serve more customers.
Further it was observed that a five-person
setup with 3 cashiers, a soup server and a
sandwich server could reduce waiting time
by over two minutes per customer. Adding a
dual-purpose server is proposed as an
alternative where customer system time
could be reduced by over one half.
The above mentioned research
investigations depict the ways various
researchers have addressed the issue of
excess waiting time at canteens and
restaurants. The studies reveal that two main
approaches; either increasing the number of
servers or proper utilization of the resources
can be implemented to optimize the
performance of any service system.
3 METHODOLOGY
3.1 Data Collection
Both primary and secondary data were
used in developing the model. Secondary
data were collected through journal articles,
books and other useful sources of
information. Those data from literature were
reviewed as a foundation for the study.
Primary data were collected on customer
arrival time, service start time and service
end time.
3.2 Sampling procedure and Population
Participants for the study were the
customers who visited the canteen from
12.30 p.m. to 1.30 p.m. on five consecutive
week days. Data collection for the study was
problematic because customers left the
queue then and there and again joined on
the way. Such data were exempted from the
simulation.
3.3 Data Analysis
The most essential data for the study
were the inter arrival times and service times
of two counters. Thus, customer arrivals for
the queue, customer arrivals to counters, and
departures from counters were noted down
in seconds using a stop watch. Accordingly,
the inter arrival times, service times for
counter 1 and counter 2 were calculated as
inputs to the software. Altogether 200 inter
arrivals times were collected.
The conceptual model for the canteen is
shown in Figure 1.
Customer arrives to the system in a
random manner. If both counters are busy,
then the customer will join the queue.
Customers will wait in the queue till one of
the counters become idle and will
immediately select the idle counter to get the
service. When all customers depart, the
counters become idle until the next arrival.
The model was implemented with
following assumptions.
 Customers select the server randomly
 A single customer gets the service only
once
 Customer arrival is independent
 Customers arrival is single
 Servers are providing a continuous
service. Customers are served in First-
In-First-Out (FIFO) basis.
The recorded inter arrival times and service
times were input to the Input Analyzer of
Arena to find the probability distributions of
3.4 Model Development

125
each data set. The obtained expressions are
summarized in Table 1.
Table 1: Distributions Obtained from Input Analyser
Data Distribution Expression
arrival to canteen Lognormal -0.5 + LOGN (27.2, 82.8)
service times of counter 1 Gamma 1.5 + GAMM (14.3, 2.28)
service times of counter 2 Lognormal 2.5 + LOGN (31.4, 31.9)
Figure 1: Conceptual Model for Canteen Queuing System
3.5 Simulation Model 1
Above data were inputs for the Arena
model. The model was run at a replication
length of one hour considering a 24 hour
day. Figure 2 is the snapshot of the current
system prevailing in the canteen. The Arena
model used Create, Decide, Process and
Dispose modules from the basic process
panel and Station and route modules from
the advance transfers panel.
3.6 Modified Simulation Models
Since the existing system did not record
optimal results, it was further developed by
changing the resources used in the counters
to serve the customers. Accordingly, model
2 was run by doubling the resources used in
counter 1. In model 3, the resources used in
the counter 2 were doubled. Finally,
resources used in both the counters were
doubled to develop the model 4.
4 RESULTS AND DISCUSSION
Table 2 summarizes the results obtained
from the four models. At first glance, it
seems that the existing model is optimal
since the percentage of customers served is
its maximum. But when compared with
model 4, model 1 has limited the entrance of
customers to the canteen. Moreover the
waiting time of customers at queues and the
number of customers waiting are minimum
in model 4. Thus it is more economical to
use model 4 than the existing one. When
resources were doubled at counter 2, model
3 revealed that the waiting time at counter 2
and the number of customers waiting at
counter 2 were reduced, but recorded higher
results at counter 1. Thus the most
economical model for the canteen system is
model 4. These results were obtained by
assuming the route time to be a minimum.
5 CONCLUSIONS AND
RECOMMENDATIONS
The main purpose of the study was to
analyze the performance measures of the
university hostel canteen. The results
showed that the performance of the canteen
was not efficient since the customer waiting
time and number of customers waiting was

ANALYZING THE UNIVERSITY CANTEEN PERFORMANCE: A CASE STUDY
126
Figure 2: Arena Simulation Model 1
Table 2: Results Obtained from Arena
higher. If this waiting time could be
reduced, the efficiency of the system can be
enhanced.
Although same resources were utilized
at both counters, still the performance at
counter 2 was not optimal as that of counter
1. Thus, the existing model was modified by
doubling the resources at the counters
separately and at the same time. The
minimum waiting time can be achieved by
doubling the resources of both counters at
once. Thus, therefore proper utilization of
resources at servers enhances the
performance of any system.
If financially feasible, another counter
can be opened at least for the lunch hour,
then the waiting time would be further
minimized.
The customer arrival time cannot be
controlled. Thus the study recommended to
control the working process and canteen
layout by properly arranging the equipment.
This study focused only on one
proposition of increasing the service
performance by increasing and properly
utilizing the resources at servers. Therefore,
increasing the number of servers in the
system can be adopted in further researches.
REFERENCES
 Aqil, M.N. (2016). Design, Simulate and
Analyze Cafeteria System using Arena.
International Journal of Mechanical and
Industrial Technology, 4, 14-24.
 Chou C.Y., & Liu H.R. (2008). Simulation
Study on the Queuing System in a Fast-
Food Restaurant. Journal of Restaurant &
Foodservice Marketing, 3, 23-36.
 Curin, S.A., Vosko, J.S., Chan, E.W., &
Tsimhoni, O. (2005). Reducing service
time at a busy fast food restaurant on
Model 1 Model 2 Model 3 Model 4
Number In 64 166 100 110
Number Out 64 120 89 108
% Customer Served 100 72.29 89 98.18
Waiting Time
Counter 01.Queue (min) 2.35 4.98 2.79 0.68
Counter 02.Queue (min) 3.03 11.96 1.43 0.89
Number
Waiting
Counter 01.Queue 1.26 7.88 2.22 0.66
Counter 02.Queue 1.62 15.27 1.31 0.75

127
campus, In WSC '05 Proceedings of the
37th conference on Winter simulation,
Orlando, 2628-2635.
 Dharmawirya M., Oktadiana H., & Adi
E. (2012). Analysis of Expected and
Actual Waiting Time in Fast Food
Restaurants. Industial Engineering
Letters, 2.
 Nsude F.I.,Uche E., & Uwabunkonye B.
(2017). Analysis of multiple-queue
multiple-server queuing system: A case
study of First Bank NIG. PLC, Afikpo
Branch. International Journal of
Scientific & Engineering Research, 8.
 Sanjay, N.A., Poluru S., & Panicker,
V.V. (2014). Simulation Modeling And
Analysis Of Student Canteen For
Service Improvement. International
Journal of Mechanical and Production
Engineering, 2, 40-42.

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