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1. QUEUEING MODELKOTA KASABLANKA MALL PARKING SPACES

2. MEET OUR TEAM
Head of Team
Aditya Nursyamsi
Coordinator of Data Analysis
Adinda AmaIia I
Coordinator of Promodel
Leo Hubertus Dimas A
Coordinator of Verification
Widi Kusnantoyo
Coordinator of Validation
Nur Annisamatin
Coordinator of Spreadsheet
Gaby Reveria Helianto
Vice Coordinator of Promodel
Bagus Novan S

3. OUTLINE OF PRESENTATION
Determine
the Problem
Model
Construction
Data
Collection and
Analysis
Model
Conceptualization
Conclusion and
Suggestion
Validation &
Verification
1 65432

4. Determine
The Problem

5. PROBLEM DEFINITION
Problem hypothesis of Kota Kasablanka Mall Parking System
And how it will affects?
If PT Secure Parking Indonesia in Kota
Kasablanka Mall ignores this problem, there will
be a decreasing number of customers in the
motorcycle parking area of Kota Kasablanka
Mall due to the difficulty in looking for parking
space.
What is the symptoms?
Since September 26th, 2015, the motorcycle parking
area in Kota Kasablanka Mall is having problem
due to the over capacity of the parking area,
queueing in arrival line, and queueing to find
parking spaces that makes customers unsatisfied.

6. OBJECTIVES OF THE RESEARCH
Determine when the peak time of arrival number in Kota Kasablanka Mall Parking
Spaces, in range 6 hours.
To know how much arrival rate and service time in Kota Kasablanka Mall Parking
Spaces when peak time
1.
2.
3.
7.
5.
To know how long the average waiting time for one visitor in counter of Kasablanka
Mall Parking Spaces when peak time
6.
To know how long queueing rate in counter of Kasablanka Mall Parking Spaces when
peak time4.
Making reccomendation related to number of server to reduce queuing time on one
customer before she/he is served in counter (into 2 minutes)
Making reccomendation related to parking capacity to reduce queuing time of one
customer before get the parking (into 2 minutes)
Making discrete event modeling and comparing with spreadsheet model
The objectives are used to answer the questions

7. Model
Conceptualization

8. MODEL CONCEPTUALIZATION
• Numb of server : 4
• Server are used : 2
• Type of server : manual
• Parking segmentation : 5
Notes
This is used as representative of real system to simplify the system

9. Data Collection
And Analysis

10. Data Collection

11. DATA COLLECTION & ANALYSIS
How we get the data?
Time
Observation
Online
Survey
1
Advanced
Judgement
What are types of data?2
Inter Arrival
time
Service Time and
Service Rate
Arrival
Rate
Find Parking
Time
Parking
TIme
TICKET
Data Collection Methodology and Types of Data
1 2 3 4 5
Direct
Measurement

12. DATA COLLECTION & ANALYSIS
When are the peak days and peak hour of Kota Kasablanka Mall Parking Spaces?
We used online survey and advanced
judgement to know when is the peak days
of parking spaces
From online survey we got Saturday as a peak day also
proved by result of advanced judgement
From the result of direct observation we got distribution of
arrival rate that showed from graphic below
1 3 1 4 8
67
16
Number of Arrival 0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
Arrival rate/hour
So the 6 peak hours are Saturday at 11.00 – 12.00,
12.00 – 13.00, 16.00 – 17.00, 17.00 – 18.00, 18.00 – 19.00

13. DATA COLLECTION & ANALYSIS
Server 1 Sever 2
For promodel
Inter arrival time
Mean 14.95 second/person 15.69 second/person
Standard Deviasi 18.34 second/person 21.02 second/person
Service Time
Mean 9.32 second/person 9.87 second/person
Standard Deviasi 3.46 second/person 3.15 second/person
For spreadsheet
Arrival rate
Mean 215.67 person/hour 216.33 person/hour
Standard Deviasi 47.92 visitors/person 72.03 person/hour
Service Rate Mean 386.39 person/hour 364.69 person/hour
Sum up all data for inter arrival time, arrival rate, service time and service rate
How we get
data?
Direct
Observation

14. DATA COLLECTION & ANALYSIS
Sum up all data for find parking time
How we get
data?
Time
Obserbation
1 2 3 4 5
Direct
Measurement
Distance of Actual
System (m)
From To Capacity
Time estimated
(detik)
17.05 Entrance
Block A 320 47.36111111
17.3 Entrance
Block B 320 47.91666667
44.6 Entrance
Block C 400 123.9444444
42.2 Entrance Block D 215 117.2777778
64.7 Entrance Block E 500 179.6111111
Total 1755

15. DATA COLLECTION & ANALYSIS
Sum up all data for find parking time
Online
Survey
How we get
data?
Time Interval
(hour)
Middle Point (minute) (Xi) Frequency (fi) Xi * fi
0.5 - 1 45 1 45
1 - 1.5 75 4 300
1.5 - 2 105 3 315
2 - 2.5 135 18 2430
2.5 - 3 165 23 3795
3 - 3.5 195 14 2730
3.5 - 4 225 15 3375
4 - 4.5 255 9 2295
4.5 - 5 285 6 1710
5 - 5.5 315 2 630
5.5 - 6 345 2 690
> 6 375 3 1125
Total 100 19440
Average Parking Time (minute) 194.4

16. Data Analysis

17. DATA COLLECTION & ANALYSIS
Identify distribustion of each type data
1
Service Time
Server 1
2
Service Time
Server 2
Log Logistic
Distribution

18. DATA COLLECTION & ANALYSIS
Identify distribustion of each type data
3
Arrival Rate
Server 1
4
Arrival Rate
Server 2
Poisson

19. DATA COLLECTION & ANALYSIS
Identify distribustion of each type data
5
Long Time
Parking
Normal
distribution

20. Model
Construction

21. MODEL CONSTRUCTION
To make a model, we should at least have 4 basic Elements
Locations1

22. MODEL CONSTRUCTION
To make a model, we should at least have 4 basic Elements (cont’d)
Entities, only consist of 1 entity, the customer itself2

23. MODEL CONSTRUCTION
To make a model, we should at least have 4 basic Elements (cont’d)
Arrivals, The arrival is divided into two, one for the front gate and the other for the backdoor gate3

24. MODEL CONSTRUCTION
To make a model, we should at least have 4 basic Elements (cont’d)
Processing, The process is quiet the most difficult of it all because it contains many step, routing, and
also logic
4

25. MODEL CONSTRUCTION
Final Model Result beginning and the end simulation
So, this is our final Model for Kota Kasablanka’s Parking System
At first one hour simulation At the last hour of simulation1 2

26. Validation &
Verification

27. Validation
Model Conceptualization Validation

28. VALIDATION & VERIFICATION
Checking by asking someone whose knowledge
of the system is trusted
Determining the truth of model flow diagram
or model logic mechanism
Trace Validity1 2
Model Conceptualization Validation
Face Validity
Tracing the truth of the model logic and
computer model (debugging)

29. VALIDATION & VERIFICATION
Trace Validity Result1
Model Conceptualization Validation
VALID

30. VALIDATION & VERIFICATION
Asked one of the Kota
Kasablanka’s Parking
Supervisor called Mr. Tri
Direct observation on the field
Model Conceptualization Validation
Face Validity2
VALID

31. Validation
Model Validation

32. VALIDATION & VERIFICATION
Model Validation
Watching the
Animation
Comparing with Other Model
Comparing output from the
simulation with other valid model
(such as spreadsheet)
1
Conducting Degeneracy and
Extreme Condition Test
Testing the model using 2 extreme
conditions
Running Traces
Stage of processes are
traced using the processing
logic model to be compared
with the actual model
2
3 4

33. VALIDATION & VERIFICATION
Comparing with Other Model
Model
Ws = 0.24 + 0.15 = 0.39
Wq = 0.24
Ls = 0.97 + 0.62 = 1.59
Lq = 0.97
Excel
Ws = 0.40485
Wq = 0.249811
Ls = 1.46
Lq = 0.8993
SERVER 11a
Model Validation

34. VALIDATION & VERIFICATION
Model Validation
Model
Ws = 0.32 + 0.16 = 0.48
Wq = 0.32
Ls = 1.22 + 0.63= 1.85
Lq = 1.22
Excel
Ws = 0.486383
Wq = 0.321999
Ls = 1.76
Lq = 1.1646
Comparing with Other Model SERVER 21b

35. Watching the Animation2
VALIDATION & VERIFICATION
Model Validation

36. Conducting Degeneracy and Extreme Condition Test
1st Extreme Condition : Occurrence 0 2nd Extreme Condition : Quantities 100
Long Queue
No Visitors
VALIDATION & VERIFICATION
Model Validation
3

37. Running Traces
VALIDATION & VERIFICATION
Model Validation
4

38. VALIDATION & VERIFICATION
VALID
Through model conceptulization
validation and model
validation, we know that the
model is valid

39. Veficiation
Model Verfication

40. VALIDATION & VERIFICATION
Model Verification
Visual verification whether the
model running has been right
Checking for code errors or
inconsistency
1 2 3Watching the Animation Reviewing Model Code Using Trace and Debugging
Facilities
• Trace : chronologically describe
what’s happening during the
simulation
• Debugger : showing the stages
of the processes in the
simulation
• Trace & Debugger enable us to
look deeper what’s happening
in the simulation

41. Watching the Animation1
VALIDATION & VERIFICATION
Model Verification

42. VALIDATION & VERIFICATION
Model Verification
Reviewing Model Code2

43. VALIDATION & VERIFICATION
Model Verification
Reviewing Model Code (cont’d)2

44. VALIDATION & VERIFICATION
Model Verification
Reviewing Model Code (cont’d)2

45. VALIDATION & VERIFICATION
Model Verification
Using Traces and Debugging Facilities2
There are no bugs, so
the model can run
perfectly

46. VALIDATION & VERIFICATION
VERIFIED
Through model verification, we
know that the model is
verified and can run properly

47. Output Analysis,
Conclusion
&Suggestion,

48. Output Analysis

49. Data at per Locations
OUTPUT ANALYSIS

50. The utilization of the D area is the highest. This is because D is the one with the least number of
Capacity, so it always appears like it is ‘full’
OUTPUT ANALYSIS

51. Location states multi
OUTPUT ANALYSIS

52. Location states single
OUTPUT ANALYSIS

53. OUTPUT ANALYSIS

54. Conclusion and Suggestion

55. These are the conclusions which also represent as the answer for the research objectives
Mall Kota Kasablanka most peaked time is
during Saturday at 12-2 PM and 4-8 PM
To answer number 2, 3, and 4 please look at the table below:
CONCLUSION
1 3 1 4 8
67
16
Number of Arrival
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
Arrival rate/hour
Server 1 Server 2
Arrival Rate 216 Visitors/Hr 217 Visitors/Hr
Service Time
9.3 sec with 3.5 sec
standar deviation
9.8 sec with 3.2 sec
standar deviation
Wq 0.25 minutes 0.32 min
Ws 0.4 minutes 0.48 min
Lq 0.9 Visitors 1.16 Visitors
Ls 1.46 Visitors 1.76 Visitors

56. These are the conclusions which also represent as the answer for the research objectives
Here is the model and the data comparison between promodel and spreadsheet
Promodel Spreadsheet
Server 1 Server 2 Server 1 Server 2
Average Number of
Customers in the Queue
(Lq)
0.97 1.22 0.9 1.16
Average Number of
Customers in the System
(Ls)
1.59 1.85 1.46 1.76
Average Waiting time in the
Queue (Wq)
0.24 0.32 0.25 0.32
Average Time in the System
(Ws)
0.39 0.48 1.46 0.48
CONCLUSION

57. Then those
statements lead to
a final conclusion
that our
Hypothesis for
Mall Kota
Kasablanka
Parking System is
DENIED
These are the suggestion for Kota Kasablanka Parking Spaces
SUGGESTION
Actually there are
nothing wrong with
Kota Kasablanka’s
Parking System.
The model do not
show a
significant
number of
waiting time
both when in the
queue for the
check in counter
(average 0.16
min) or when they
search for a
parking spot (1
min)
The current
number of server
used is also not
really a problem,
because there’s not
a significant
number of people
in the queue. So,
we think it’s best
for now just to use
one server per
gate.
The current total
capacity is 1755
spots, which is a
huge number of
capacity for a
parking lot. So, for
now Mall Kota
Kasablanka do
not have to add
the capacity of the
parking lot,
because it is
already sufficient

58. THANKYOU