1. The document describes a computer simulation model of the Subway food service location at Wayne State University to minimize customer wait times. 2. The current model shows high wait times, especially at the salad station, but scheduling employees differently reduces wait times significantly. 3. The proposed model schedules two employees each at the bread and salad stations instead of just one, lowering average wait time from 18 minutes to just over 0 minutes.

1. Computer Simulation Methods
“Reducing Queue Time” at
using Arena
located at
Wayne State University Campus
Abhishek Anand
Parag Kapile

2. Flow chart for Restaurant

3. Objective
• To minimize the total lead time, wait time of
customers and number of customers waiting
at Subway food services located at the Wayne
State University campus.
• Scheduling additional employees and cashier
and comparing the proposed model with
current model.

4. Motivations and why you want to
work on this problem?
• We work as student worker at Subway food
services at Wayne State University.
• We have noticed that during peak hours the
wait time for customers in queue is large
which delay the order processing time and
delay in service thus leading to unsatisfied
customer.

5. Model Description
• Customer here is an entity and arrival rate is defined
by Normal distribution curve.
• We have nine employees working according to the
schedule.
• There 4 stations:
1. Bread and Meat selection station.
2. Toaster
3. Salad and Dressing station.
4. Paying and wrapping station.

6. Model Description
Model in Arena

7. Input data analysis
• Using Input Analyzer, we found the best fit
curve for the data.
• The distribution for arrival was found to be
Beta distribution.

8. Input Analyzer

9. Station and Route
• As the customer arrives they take 25 seconds
to reach at the Bread and Meat selection
Station.
• And it takes 6 seconds for transfer between
other stations.

10. Schedule
• We are using the real time schedule of
subway.

11. Resources
• Resources are created using the real time
schedule.

12. Bread and Meat Selection Process
• We are using Set.
• Set is usually a set of resources which can hold
multiple elements.
• Resources here are employees.
• Prep A set is the set name for bread and meat
selection process.

13. Toasting Process
• One toaster unit is present.
• 80% people want their sub to be toasted.
• Toaster takes constant time.

14. Salad and Dressing Process
• Similar like bread and meat selection process
we are using Prep B set for salad and dressing
process.
• And we are using Prep C set for paying and
wrapping process.

15. Animation Model

16. Output Statistical Analysis
CURRENT MODEL:
• Abhishek is schedule for both Station 1 (BMS)
and Station 3 (SDS).
• According to current model the wait time in
the Queue is 18.26 minutes and number of
customers waiting in the Queue is 7.6 for
Salad and Dressing Selection Process.
• The bottle neck for the current model is Salad
and Dressing Selection Process.

17. Current Model : Queue

18. Proposed Model
• Abhishek and Rohan are schedule for Station 1
(BMS) and Pratik and Arpit are schedule for
Station 3 (SDS).
• According to proposed model the wait time in
the Queue is 0.035 minutes which has been
reduced from 18.26 minutes and number of
customers waiting in the Queue is 0.014 which
has been reduced from 7.6 for Salad and
Dressing Selection Process

19. Proposed Model Queue

20. Recommendation
For First Shift:
• Schedule Abhishek and Rohan for Station 1 (BMS)
instead of scheduling only Abhishek.
• Schedule Pratik and Arpit for the Station 3 (SDS)
instead of scheduling only Abhishek.
For Second Shift:
• Schedule Cook and Akshay for Station 1 (BMS)
instead of scheduling only Cook.
• Schedule Nara and Dejia for the Station 3 (SDS)
instead of scheduling only Nara.

21. Conclusion
• There is significant change in the current
model and the proposed model.
• We recommend to use the proposed schedule
for decreasing the wait time in the Queue and
also increasing the utilization of employees.
• We can further reduce the average wait time
and number of people in queue by adding
another line comprising of all the 4 stations.