A simulation-based optimization project conducted in 2009. The team implemented Rockwell Automation's ARENA simulation software.

1. SIMULATION AND MODELING OF
CLEMSON DRY CLEANERS
USING ARENA
Project Advisor
Dr. Kevin Taffee
Project By
Indraneel Dabhade
Rohit Shivamallu
Industrial Engineering Dept.
CLEMSON UNIVERSITY

2. PROJECT DESCRIPTION AND PROBLEM STATEMENT
The Clemson Laundry And Dry Cleaners on Anderson
Highway services more than 100 customers per day.
To improve the customer satisfaction, we had to reduce
the probable delivery date.
Customers wanted a cloth pick-up and delivery system.
We have developed an alternate model to reduce the
waiting time in the above processes.

3. The aim of this project was to improve the laundry facility
so as to reduce the queue of clothes going to the
different processes. We also responded to the customer
requests of introducing a ‘Pick-up & delivery’ facility for
the system.
An Arena model is developed to determine the
performance of the existing system. Based on OPT
Quest results, we have developed an ‘alternate’ for the
system. The new model has a cloth pick-up & delivery
facility.
PROJECT OBJECTIVE

4. Model is simulated for 5 replications with each simulation having 150
working hours.
Only the clothes that are brought in by the pick-up service will be
delivered.
The clothes brought in by customers will be collected by the
customers themselves.
There are no machine down-times or idle time.
The clothes are loaded instantaneously after every cycle.
The concentration of customers in and around Clemson has been
divided into 4 zones for Pick-up & delivery.
BASIC ASSUMPTIONS

5. THE FACILITY

6. VARIOUS PERFORMANCE MEASURES THAT WERE CONSIDERED
Number Waiting in Tagging Process.
Number Waiting in Dryer Station.
Number Waiting in Hanger Station.
Total Number of clothes coming out
COSTS
Extra Staffing Cost
Extra Machine Cost
PERFORMANCE MEASURES

7. Customer Arrival Time
Types of clothes brought in and the process the clothes need
to go through
Number of clothes per Customer
Processing Times for each of the different processes
Cost for an extra staff
Cost for extra machines
Concentration of customers in and around Clemson
DATA COLLECTED

8. DATA COLLECTED
PROCESS TIMES
Process Process Time (in Mins.)
(Constant)
Laundry Cycle 80
Dry Cleaning Cycle 80
Ironing (Pants) 0.75
Ironing (T-Shirt) 0.5
Ironing (Jackets) 1
Dryer 30

9. INPUT ANALYSIS

10. The input data has been analyzed using the Input Analyzer. All the
data was subjected to analysis and the corresponding distributions
were used as inputs in the CREATE and DECIDE modules.
Total number of clothes for Ironing : 715 = 53 %
Total number of clothes for Laundry : 500 = 38%
Total number of Clothes for Dry Cleaning : 126 = 9%
Total : 1341
INPUT ANALYSIS
(ON THE DATA COLLECTED )
TOTAL CUSTOMERS TOTAL CLOTHES
Distribution: Lognormal Distribution: Normal

11. INPUT ANALYSIS
(ON THE DATA COLLECTED )
CLOTHES FOR LAUNDRY
LAUNDRY JACKETS
LAUNDRY PANTS
LAUNDRY SHIRTS
Distribution: Beta
Distribution: Beta
Distribution: Beta

12. INPUT ANALYSIS
(ON THE DATA COLLECTED )
CLOTHES FOR LAUNDRY
CLOTHES FOR IRONING
IRONING PANTS
IRONING TOTAL
IRONING SHIRTS
Distribution: Beta
Distribution: Normal
Distribution: Beta

13. INPUT ANALYSIS
(ON THE DATA COLLECTED )
CLOTHES FOR DRY CLEANING
DRY CLEANING PANTS
DRY CLEANING SHIRTS
Distribution: Erlang
Distribution: Beta

14. ARENA MODEL

15. Arena Base Model is divided into 4 sub-models:
Customer arrival
Tagging and separation
Clothes processing
Clothes delivery
THE ARENA MODEL

16. CUSTOMER ARRIVAL
THE ARENA MODEL

17. THE ARENA MODEL
TAGGING AND SEPARATION

18. THE ARENA MODEL
TAGGING AND SEPARATION

19. THE ARENA MODEL
CLOTHES PROCESSING

20. THE ARENA MODEL
CLOTHES PROCESSING

21. THE ARENA MODEL
CLOTHES PROCESSING

22. THE ARENA MODEL
CLOTHES DELIVERY

23. BASE MODEL OUTPUT
(TOTAL NUMBER SEIZED)

24. BASE MODEL OUTPUT
(TOTAL NUMBER WAITING IN QUEUE)

25. OPT QUEST ANALYSIS

26. Number of Simulations run : 31 simulations
Number of Replications run :10 replications
INPUT FOR OPT QUEST
Resource Upper
bound
Suggested
Value
Lower Bound
Dry Cleaner 5 1 1
Hanger 5 1 1
Tagging Counter 6 1 1

27. Constraints
{(Name of resource*cost of resource)<=$8000}
{(1700*Dry Clean )+ (700 * Hanger) + (1000* Tagging Counter) <=8000}
{Number in Batch for Dry cleaner machine queue<=40}
{Number in Tagging Queue <=30}
{Number in Delivery Hanger Queue<=40}
CONSTRAINTS FOR OPT QUEST
Objective
Minimize :
[Number In Dry Cleaning batch Queue] + [Number In Delivery Hanger Queue]
+ [Number In Dry Cleaning Process Queue] + [Number In Tagging Process
Queue]

28. Resource No. available
(base model)
No. Suggested
(modified model)
Laundry Machine 3 3
Dry Clean Machine 1 1
Dryer Machine 1 1
Dry Clean T-Shirt Press 2 2
Dry Clean Pant Press 2 2
Shirt Press 5 5
Pant Press 2 2
Jacket Press 2 2
Tagging Counter 1 3
Hanger Counter 1 4
NEW RESOURCES AS SUGGESTED BY OPT QUEST

29. PROCESS ANALYZER

30. INPUT TO THE PROCESS ANALYZER
Main Screen

31. INPUT TO THE PROCESS ANALYZER
Delivery Hanger

32. INPUT TO THE PROCESS ANALYZER
Number Out

33. INPUT TO THE PROCESS ANALYZER
Tagging Process

34. INPUT TO THE PROCESS ANALYZER
Batch of Clothes for Dry Clean Process

35. MODIFIED MODEL

36. MODIFIED MODEL OUTPUT

37. MODIFIED MODEL OUTPUT
(AFTER IMPLEMENTING SUGGESTIONS FROM OPT QUEST)
Number OUT 11,723 15,073
% Increase =
29%
Queue
(Batch for dry clean + Hanger +
Tagging Counter)
39+1521+111
=1671
39+10+25
= 74
% Decrease =
95.57%

38. MODIFIED MODEL OUTPUT

39. MODIFIED MODEL OUTPUT

40. OUTPUT ANALYSIS

41. OUTPUT ANALYSIS
COMPARISON OF MEANS --- BATCH FOR DRY CLEANING PROCESS

42. OUTPUT ANALYSIS
COMPARISON OF MEANS --- DELIVERY HANGER

43. OUTPUT ANALYSIS
COMPARISON OF MEANS --- TAGGING PROCESS

44. The optimal solution has been obtained by extensively
using OPT Quest. We have found that the laundry
facility needs to have 1 Dry Cleaning machines, 4
Hanger stations and 3 Tagging stations. The ultimate
objective of reduced queue at the processes has been
obtained in the alternate model and we can say that,
the customer is satisfied, as a result of this.
CONCLUSION

45. We would like to take this opportunity to thank
Dr.Kevin Taffee for providing us an opportunity to work
on such an interesting and thought simulating project.
We would also like to thank him for all the support and
time he has given us, throughout.
We would like to thank the staff of Clemson Dry
Cleaners, Clemson, SC., for providing us with the
required data and educating us on the facility
operation & layout.
ACKNOWLEDGEMENTS

46. Thank You!!
Questions??