The Project is done as a final project for the course BANA 7030-Simulation Modelling where the focus is in understanding the basics of simulation modelling using Rockwell Automation’s “Arena”. The goal of the project is to study working of the Shell gas station and food mart at 3337 Clifton Ave, using Arena simulation and increase the resource utilization of the resource or the pumps. The Shell Petrol gas station is a facility that sells fuel and engine lubricants for motor vehicles. Also, along with gas station there is also a Food Mart which is a located in the same premise as the gas station, which is basically a convenience store. The model uses the layout, operation and resource allocation of the gas station and the food mart etc in Arena to simulate the real-life scenarios.

1. On my honor, I have neither given nor received unauthorized aid in completing this academic works
M 12388639
Simulation of the Shell Gas Station and
Shell Food Mart using Arena
Submitted by:
Ayank Gupta
M12388

2. Contents
CHAPTER 1: INTRODUCTION.................................................................................................................3
1.1 Project Synopsis......................................................................................................................3
1.2 Problem statement.................................................................................................................3
1.3 Assumptions............................................................................................................................4
Chapter-02: Data Collection and Data Fitting.........................................................................................5
2.1 Data collection ..............................................................................................................................5
2.2 Fitting the data to distribution:.....................................................................................................5
Chapter 03: Arena Modelling..................................................................................................................9
3.1 Modelling the systems..................................................................................................................9
Chapter 04: Results and Interpretation................................................................................................16
4.1 Results.........................................................................................................................................16
4.1.1 By entity:..................................................................................................................................16
4.1.2 By Queue..................................................................................................................................17
4.1.3 By Resource:.............................................................................................................................17
4.1.4 By user specified ......................................................................................................................18
4.2 A few interpretations on the results:..........................................................................................18
Chapter 05: Alternative Scenarios and improvements in the System..................................................19
5.1 Alternate Scenarios.....................................................................................................................19
5.2 Process Analyser Scenarios.........................................................................................................20
Chapter 06: Conclusion and Suggestions..............................................................................................22
Reference..............................................................................................................................................22

3. CHAPTER 1: INTRODUCTION
1.1 Project Synopsis
The Project is done as a final project for the course BANA 7030-Simulation Modelling where the
focus is in understanding the basics of simulation modelling using Rockwell Automation’s “Arena”.
The goal of the project is to study working of the Shell gas station and food mart at 3337 Clifton Ave,
using Arena simulation and increase the resource utilization of the resource or the pumps.
The Shell Petrol gas station is a facility that sells fuel and engine lubricants for motor vehicles. Also,
along with gas station there is also a Food Mart which is a located in the same premise as the gas
station, which is basically a convenience store.
The model uses the layout, operation and resource allocation of the gas station and the food mart
etc in Arena to simulate the real-life scenarios.
The input data consists:
• Customer arrival rate for different time-period during normal and peak hours of the day.
• Service time at the gas station.
• Shopping or Merchandising time at the food mart
• Service times at
o The cash counter
o The Coffee vending machine
o The ATM withdrawal machine
The model prepared was run for 30 replications to analyse the result, based on which changes were
made to the existing model and suggestions are made to improve the efficiency of the shell gas
station complex.
1.2 Problem statement
The Shell petrol pump and food mart is open 6 AM- 1 AM. It has its maximum arrivals during the
office hours for both the morning and evening. At present the shell gas station has 8 resources or
pumps for filling the gas whereas there are presently 2 cashiers at the food-mart. Due to low arrivals
the Resource utilisation is a bit poor. On the other hand, resources in the food mart are also under
utilized
Shell spends a considerate amount on the maintenance of these resources and on hourly pay to the
workers.
Our Propose is to decrease the capacity of the resources, our motive here is to study the effect of
resource reduction on the below KPIs:
• The average waiting time of the consumer in the queue.
• The Average number of people left due big queue (more than 3 cars in the waiting queue)
• Resource Utilisation

4. 1.3 Assumptions
The existing system at Shell gas station and food mart could not completely mimic the actual
working of the system due to the natural variations and unscheduled activities. But the
results/statistical inference that we get through simulation is helpful in analysing the system and
making inference based on it.
Here are the assumptions made in the model:
1. The System (Gas station / Food Mart) is open 19 hours a day.
2. There are no breaks for the workers in the model.
3. The gas station pumps are assumed to work without a failure or breakdown.
4. The time taken by the consumer to travel anywhere between the system is assumed to be zero.
5. The distribution of service time is assumed to same w.r.t the process for all the resources
6. The probabilities in the decision modules are assumed based on common sense and
observation.
7. A consumer entering food mart will only perform one of the following task
a. Shopping
b. Coffee collection
c. ATM withdrawals
8. All the gas station resources are assumed to be equally likely to be picked by the consumer for
filling their gas.
9. A consumer who takes both gas and visits the food mart. Will necessarily visit the gas station
first.
10. Not adding the air pump system to the arena model to reduce complexity of the model.
11. If a consumer finds the queue length of petrol filling line is more than 3 will leave without
fuelling the tank. (Dissatisfied customer)
All the assumptions hold valid for the time when the model is running

5. Chapter-02: Data Collection and Data Fitting
2.1 Data collection
To input the service times for these different resources, data was collected at the shell gas station
premises in the form of inter-arrival times of customers and the service times for the resource
allocated at the various points with the permission of shell authority. The data used here was
collected for between 5 pm and 8 pm for three days (The days were either Monday, Thursday or
Saturday).
Following data was collected on this system:
➢ Consumer arrival rate for different time periods during food mart working time
➢ Service time at the gas filling station
➢ Service time at the food mart
o For cash counter
o For shopping merchandising
2.2 Fitting the data to distribution:
The very next step was to fit the collected data into proper distributions. Arena input analyser was
used for achieving this for all petrol filing process, Shopping/Merchandising delay and cash counter
process. The Arena model takes the input data to run and analyse through different modules in the
model. Arena accepts the data in the form of a distribution which will be best fitted for the raw data.
The raw data of service times for all the resources are fitted into a distribution which is fed into
Arena. The data was loaded in the form of a text file with raw data which was then fit to a suitable
distribution. Please find below the distribution of the service times at various points of the model.
Consumer arrival rates:
For getting the consumer arrival rates, we have leveraged the data points that were collected for the
consumer arrival rate for 3 hours in 3 different days.
To get a distribution of consumers arrival in the food mart for all the 19 hours in a day, I used the
information for the distribution provided by google in the below website and used my data points to
form a similar distribution.
Google maps link to consumer arrival rate distribution

6. Here are below the different arrival rates at different timings:
Table 1
Here is a Screen shot of the scheduled arrival as a histogram
Fig 1
6AM - 7AM 6
7AM - 8AM 14
8AM - 9AM 26
9AM - 10AM 32
10AM - 11AM 40
11AM - 12PM 40
12PM - 1PM 30
1PM - 2PM 24
2PM - 3PM 40
3PM - 4PM 50
4PM - 5PM 56
5PM - 6PM 38
6PM - 7PM 38
7PM - 8PM 16
8PM - 9PM 10
9PM - 10PM 6
10PM - 11PM 12
11PM - 12AM 10
12PM - 1AM 4

7. Processing time for the gas station filling
Fig 2
Processing time for the ATM withdrawals
As I was not able to collect enough data points for the process. With some common sense and alittle
observation I am assumping its distribution as TRIA(1,1.5,2.5)
Processing time for the Food Mart cash counters
Fig 3

8. Distribution of the shopping/Merchandising process:
Fig 4
Processing times for the Coffee Vending Machine
Since I was not able to find enough data points I am assuming the distribution as
TRIA(1,1.5,3) with some observation and common sense

9. Chapter 03: Arena Modelling
3.1 Modelling the systems
The Shell gas station system was divided into multiple small pieces to prepare the model in Arena.
To simulate the real-life scenario, various modules are used, for instance Create, Process, Decide,
Assign etc.
To have a better understanding of the arena model in general, let’s study the model with the help of
below steps.
1. Consumer enters the Shell gas station complex.
a. Consumer either enters shell gas station or Food Mart
2. Consumer waits in the queue to get the gas refilled if any.
3. Consumer steps outside the car, gets his fuel refilled and pays his bill
a. At this point consumer can exit the system or can enter the Food mart.
4. Consumer (either from gas station or for food mart only) follows any one of the three steps
a. Shop/Merchandise
b. Getting Coffee (waits in the queue if any and then uses the machine to get coffee)
c. Withdrawing from ATM (waits in the queue if any and then uses the machine to get
coffee)
5. Consumer from 4a and 4b heads towards cash counter to pay their bill and wait in the queue
if any.
6. Now consumer in step 5 and step 4 c exits the shell gas station complex
Here is a snapshot of the Arena Module
Fig 5

10. To explain the model parameters stepwise we will go through each step looking closely into the
module and logic used to build the module
Below are the modules incorporated in the module
1. Consumer Enter the System: -
The consumer enters the system with a create module that feds entities into the model at
arrival rates with the help of scheduled mentioned in the previous section.
Fig 6
A decision module is used after the create module, based on the observation 60% of the consumer
enters the gas station and remaining 40% goes directly to the food mart for shopping.
Fig 7
Now the consumer moving towards the gas station finds if the queue length is less than 4 will stay in
the queue other wise will leave the station.
We are achieving this with the help of decision module and then we are using a record module to
calculate the number of cars left (The same metric will be used later for our statistical analysis and
deciding the best scenarios)

11. Fig 8
We use the record module as counter to count the consumers left without getting their fuel refilled.
We will call these consumer as the dissatisfied consumer
Fig 9
2. Consumers enter the Gas Filling Queue
The customer joins queue if there is any, and waits for their turn. The queue is shown in a
blue outlined box in the model animation.
3. Consumer starts with the filling process
The consumer steps out of the car, pays the bill with debit/credit card and starts to fill the
gas to his car. To show this, we add a seize delay release module.
The consumer is free to choose from any of the available resources out of the 8 petrol
pumps. The processing time used in the module is the one we got from the input analyser.

12. Fig 10
After filling the gas consumer decides whether to leave the premise or enter the food mart. We do
that with the help of another decision module. Based on our observation, we assume 35% of the
time the consumer parks his vehicle and enter the food mart to buy some thing
Fig 11
4. Consumer enters the food mart
Before entering the consumer decides whether to go for shopping merchandising or grab a coffee or
to withdraw money the ATM. After the processing the consumer will move towards the cash counter
to pay their bill except the ATM withdrawals process after this the resource will leave the system.
Note: This is an assumption that a consumer will either shop or buy coffee or withdraw money from
ATM.

13. The above task is done in arena with a help of decide module. It is assumed with help of observation
that 65% of the people shop at food mart and 20 % of the consumer purchase coffee from the shop
and around 15% of the people enter the system to withdraw money.
Fig 12
Shopping Delay module
The consumers enter the system and without occupying any resource and shops. The delay process
follows the distribution that we have received through input analyser.
Fig 13
Similarly, the consumer enters the ATM withdrawal and seizes the ATM machine. The processing
time is same as the distribution used in the in the input analyser to fit the data.

14. Fig 14
After the entity completes the coffee process and shopping, they move towards the cash counter.
The process is shown through a seize delay release module with 2 resources working parallelly in a
shift of 9 hours.
Fig 15
Consumer leave the system
Consumer will leave the system after refuelling the gas or shopping at the gas station or both.

15. Simulating the model
The run setup is used to run 30 replications of the system where is each replication runs for 19 hours
a day.
Below is the Screen shot of the Run setup module
Fig 16
Here is the Screen shot of the Animation model of Arena
Fig 17

16. Chapter 04: Results and Interpretation
4.1 Results
The Arena produces a detailed result for us to view after the model has run to completion.
The results are shown by Entity, Queue, Resource and User defined sections. Some additional
reports can also be generated according to one’s need.
For the model the average number out of the shell gas station complex in a simulation run for 19
hours a day with replications of 30 days is 492
Fig 18
4.1.1 By entity:
Arena gives a detailed result regarding various time with Average value, Maximum value and
minimum value etc. Along with that Arena also give Number of Entity in and out of the system.
Below are the screen shots of the output below:
Fig 19

17. Fig 20
Note: On Average 492 customer enters the shell gas station complex and on an average 6 to 7
customers are present in the complex at a time
The Average total time of the consumer in the system is 14.51 min.
4.1.2 By Queue
Arena gives us the waiting time and number of entities waiting in queue in the model.
We observe waiting time at the queue is minimal.
Fig 21
4.1.3 By Resource:
Resource utilization is one of the important for our analysis.
We observe the resources in the gas station is underutilized and will try to reduce the resources in
our alternate model so that we are able to increase the resource utilization of the resources.

18. Fig 22
4.1.4 By user specified
Arena gives the user the freedom to specify user specified attribute which can be seen in the
published results. We have created a car left counter lets see its result below.
Fig 23
4.2 A few interpretations on the results:
1. As mentioned, the scheduled utilization of the petrol pump resource and the food mart
resource is on the lower side and our main aim will be to increase the utilization of the
resources.
2. Waiting time in the queue doesn’t seems to be a problem for shell gas station as the average
waiting time in the queue is small
3. The number of cars leaving the complex is also small (N= 1 to 2) which shows only a small
fraction of the consumers are leaving if the queue length is more than 3.

19. Chapter 05: Alternative Scenarios and improvements in the
System
5.1 Alternate Scenarios
Scenario 1: 1 Resource reduction for gas station and changed scheduling of food mart resource.
The capacity of the gas station was decreased by 1 resource and the capacity of the food mart was
reduced to 1 during the non-peak hours and have them back on during the peak hours as compared
to the base model.
Scenario 2: 2 Resource reduction for gas station and changed scheduling of food mart resource.
The capacity of the gas station was decreased by 2 resources and the capacity of the food mart was
reduced to 1 during the non-peak hours and have them back on during the peak hours as compared
to the base model.
Scenario 3: 3 Resource reduction for gas station and changed scheduling of food mart resource.
The capacity of the gas station was decreased by 3 resources and the capacity of the food mart was
reduced to 1 during the non-peak hours and have them back on during the peak hours as compared
to the base model.
Resource Scheduling for the food mart resource during the peak hours:
Before: Original Model After Resource capacity allocation (Peak hours)
Fig 24

20. 5.2 Process Analyser Scenarios
Below is the screen shot of the process analyser window.
Fig 25
Average Resource scheduled utilization by the resources:
The graph for the below scheduled utilization below
Fig 26
We observe the scheduled utilization increases by almost by 48.5 % for the Scenario 4 as compared
to the original configurations.
Dissatisfied customer counter
It is the average of the cars that left without refuelling as the length of the queue was more or equal
to 4

21. Fig 27
So, we observe the best case in this respect is undoubtedly original scenario.
Average Wait time
Fig 28
We observe that the average wait time is the lowest in case of bases case, which was as expected.

22. Chapter 06: Conclusion and Suggestions
The Shell gas station at 3337 Clifton Ave, Cincinnati, Ohio was modelled in Arena Simulation
Software and the results about the relevant parameters were generated. A detailed analysis was
done on the output of the Arena module and it was observed that the resource utilisation of the gas
station and food mart resources were small. Our objective is to increase the utilisation of the
resources with-out increasing the Dissatisfied customer counter and average waiting time by much.
The motivation behind increasing the utilization of the resource is to increase the profit of Shell by
reducing the cost spend in maintenance of the gas pumps and per hour salary to the worker in the
food mart.
Different tools were used in Arena to design the inputs and to analyse the output generated by
Arena. We have used input analyser to fit the distribution of the data and process analyser for the
statistical analysis by comparing various scenarios.
We have implemented 3 different alternate scenarios for understanding the change of resource
allocation on the system. This was achieved by scheduling the capacity of the food mart workers by
reducing the capacity of the resource from 2 to 1 and adding an extra resource only during the peak
times. We observed that the best result with respect to all the three metrics was found in the
scenario 3(refer fig 25).
Since we have a trade off as we increase the resource utilisation, we will also be increasing the
average wait time and average dissatisfied customer (which is not desirable).
But in this case, we need to find the best compromise among all the scenarios.
We observe that, for Scenario 3 we can increase the scheduled utilisation by 30% and increase the
waiting time to 0.829 min and increase the dissatisfied customer from 2 to 5. (refer fig 25)
Based on the analysis we suggest decreasing the gas station resource by 2 and schedule the food
mart resource to use an extra worker during the peak time and reduce 1 worker overall.
There can be many suggestions on the modifications in the model to optimize the output both
economically and commercially, but we have discussed only one of them.
Reference
1. Content – Simulation with Arena 6/e- W. David Kelton- University of Cincinnati, Randall P.
Sadowski, Nancy B. Zupick, Rockwell Automation
2. Data – regarding the arrival rate distributions
Google maps link to consumer arrival rate distribution
3. Image – Image Link