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PARKING SYSTEM MODELLING
of Mall Ambassador
Group 7
Anindya Alfi Septyanti (1306448110)
Anggi Hazella (1306370051)
Felisa Fitriani (1306369945)
Nadila Aristiaputri (1306393023)
Natasya Sheba S (1306370146)
Timotius Alfin (1306409633)
Dosen Pembimbing : Arry Rahmawan, ST, MT
2
Mall Ambassador
Capacity
360 parking spots.
Parking Lot
Has 3 basement levels of
parking lot.
Location
Jl. Prof.Dr.Satrio No. 14,
Kuningan, Jakarta Selatan,
Banten 15810, Indonesia
Working Hours
10.00 – 22.00
Mall Ambassador is one of the most favorite place for people in
the terms of buying electronics and shopping clothes. Among other
malls and shopping centers that also sell variety of electronics,
Mall Ambassador gives more affordable prices than other places.
Branded cheap clothes can also be found here.
3
4
Problem
Formulation
Model
Conceptualization
Formal System Modelling Methods
Data Collection and
Analysis
Model Construction Validation and
Verification
1 2 3 4 5
Project Report and
Presentation
6
Problem Formulation
6
With such high visitors coming to Mall
Ambassador and many of them drive cars,
this Mall only provides small spaces for
parking – only 360 parking spots available
– causing a queue when entering the parking
spot and difficulties in finding the parking
spot, especially on peak time.
Problem Statement
7
Make the model based on the data we
obtained from observation. Based on the
model we can conclude whether the
parking system of Mall Ambassador is
already optimum and met the criteria that
had given previously.
Objectives
Model Conceptualization
9
Data Collection and Analysis
11
Determining data
requirement
Source of data
Analyze the data
using software
Step of Data Collection
12
Arrival time
Quantity of parking areaService time
Arrival rate Pattern of parking area
Distribution
Number of servers
Duration time at the Mall
Data Requirement
Searching time
Working hour
Peak hour
What do we need to do simulation?
13
System Documentation
We made a documentation of
layout and quantity parking
area
Personal Observation
We did personal observation
such as direct observation and
made a questionnaire
Personal Interviews
We interviewed Mr. Zaeni as
Head of Parking Area at
Ambassador Mall
Resource of Data
Where are the data come from?
14
Mall Ambassador Jakarta
Place
26 September 2015
Date
10.00 am – 10.00 pm
Time
Observation
Result of Data Observation
15
16
Data Processing in Observation
We observed from 10.00 am
until 22.00 (12 hours), then we
processed data to determine
peak hours in Ambassador Mall.
The result is from 11.00-17.00
is peak hours in Ambassador
Mall
131
228
255
195
158
187
144
127
83 79
66
35
0
50
100
150
200
250
300
Number	
  of	
  Car
Arrival	
  Rate
Data Processing in Observation
228
255
195
158
187
144
0
50
100
150
200
250
300
Arrival	
  Rate	
  in	
  Peak	
  Hour	
  
avg	
  arrival	
  
rate
7.20
5.40 5.51
6.24
5.44
5.84
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
Service	
  Time	
  in	
  Peak	
  Hour
avg	
  service	
  
time	
  
15.79
14.07
18.44
22.76
19.04
24.95
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Inter	
  Arrival	
  Time	
  in	
  Peak	
  Hour
avg	
  inter	
  
arrival	
  time	
  
17
18
Spreadsheet
19
Data Analysis in Observation
Arrival Rate Service Time
20
Data Analysis in Observation
Searching Time Inter Arrival Time
21
Questionnaire
The questionnaire consists of:
01 How often do they go to Mall Ambassador
02 Time interval in entering the Mall Ambassador
03Duration of parking time in Mall Ambassador
04 The length of waiting time in queue
22
Result of Questionnaire
20
44
21
8
0
5
10
15
20
25
30
35
40
45
50
10.00-­‐12.00 12.00-­‐15.00 15.00-­‐18.00 18.00-­‐21.00
Arrival	
  TimeQuestionnaire
Arrival	
  Time
10.00-­‐12.00 20
12.00-­‐15.00 44
15.00-­‐18.00 21
18.00-­‐21.00 8
Time	
  Duration
1-­‐2	
  jam 27
2-­‐3	
  jam 50
>3	
  jam 16
27
50
16
0
10
20
30
40
50
60
1-­‐2	
  jam 2-­‐3	
  jam >3	
  jam
Time	
  Duration
23
Data Analysis in Questionnaire
This distribution shows the
distribution of data from
questionnaire is normal
distribution.
1
2
3
4
5
67
89
25
Attachments
Form Online Questionnaire
Model Construction
27
Model Construction
Put a layout as
background for the
model
Build location and
Location Logic for
the model
Build Entities and
Entities Logic for the
model
Build Process and
Process Logic for
the model
Build Arrivals and
Arrival Logic for
the model
1 2 3 4 5
Run the model
6
Entrance
Queue
Locket
Parking Area
Exit
Put a layout as background for the model
28
Build Location and Location Logic for the model
29
30
Build Location and Location Logic for the model
Build Entities and Entities Logic for the model
31
Build Process and Process Logic for the model
32
Build Arrivals and Arrivals Logic for the model
33
Run the model
34
Click to watch the video of model simulation
Validation and Verification
36
01 Watching the animation
02 Comparing with other models
03Conducting degeneracy and
extreme condition test
Validation
We use these following techniques
for validating the model:
04 Performing sensitivity
analysis
05Running trace
37
Watching the Animation
After the model was done, we have to
run the model to see whether the model
is correct or not.
Comparing with Other Models
We have to make sure the model is correct by
comparing with the excel data. After we run the model,
we can see the statistic, if the number is the same with
the excel calculating, then our model is finally correct
38
39
40
Conducting Degeneracy and
Extreme Condition Test
In conducting degeneracy and extreme condition test
we can change the arrival rate. Assume that we
change the arrival rate to 0 (zero) following with the
accuracy, then the result should be:
No car arrive in the entry queue. If
it is happened, then our model is
finally correct
In performing sensitivity analysis, we can try to change the service
time, if we change into the smaller number of service time there
will be no queue, if we change into bigger number there will be
queue. After we try, our model adjust with the changing of
numbers, it means the model is correct
Performing Sensitivity Analysis
41
42
Running Trace
Running trace will show all of the event on the discrete model.
There is no error on our model, based on the trace results
43
01 Reviewing model code
02 Checking for reasonable input
and output
03Watching the animation
Verification
In order to verified the model, we use some techniques:
04 Using trace and debugging
facilities
Reviewing Model Code
44
Checking for Reasonable Input and Output
45
The number of entry car is same as the number of exit car and
the number of car at the current location, so input and output
are reasonable.
Watching the Animation
We can know that the model is correct if the model is running
until it’s done correctly and without bug.
46
47
Using Trace and Debugging Facilities
We use trace and debugging facilities to make sure we build the
model correctly implemented with good input and structure.
There is no error on our model, based on the trace results. There is no
debugging in our model.
Results and Recommendations
49
01 The peak time is at 11.00-17.00
02
-Arrival Rate at peak Time:
P(195;41.8) sec
-Service Time at peak Time:
L(5.94;0.69) sec
03Average waiting time in
queue: 0.15 sec
Results
Our Result is:
04 Average cars in queue: 0.01
05
The data from spreadsheet is
same with the data from
promodel
50
01
The capacity should be
increased by 25 parking slot to
meet the requirement.
02
The number of ticket locket is
enough to meet the
requirement.
Recommendation
Our Recommendation is:
THANK YOU!

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Contoh Desain Slide Presentasi Ilmiah Kreatif dan Menarik #4

  • 1. PARKING SYSTEM MODELLING of Mall Ambassador Group 7 Anindya Alfi Septyanti (1306448110) Anggi Hazella (1306370051) Felisa Fitriani (1306369945) Nadila Aristiaputri (1306393023) Natasya Sheba S (1306370146) Timotius Alfin (1306409633) Dosen Pembimbing : Arry Rahmawan, ST, MT
  • 2. 2 Mall Ambassador Capacity 360 parking spots. Parking Lot Has 3 basement levels of parking lot. Location Jl. Prof.Dr.Satrio No. 14, Kuningan, Jakarta Selatan, Banten 15810, Indonesia Working Hours 10.00 – 22.00
  • 3. Mall Ambassador is one of the most favorite place for people in the terms of buying electronics and shopping clothes. Among other malls and shopping centers that also sell variety of electronics, Mall Ambassador gives more affordable prices than other places. Branded cheap clothes can also be found here. 3
  • 4. 4 Problem Formulation Model Conceptualization Formal System Modelling Methods Data Collection and Analysis Model Construction Validation and Verification 1 2 3 4 5 Project Report and Presentation 6
  • 6. 6 With such high visitors coming to Mall Ambassador and many of them drive cars, this Mall only provides small spaces for parking – only 360 parking spots available – causing a queue when entering the parking spot and difficulties in finding the parking spot, especially on peak time. Problem Statement
  • 7. 7 Make the model based on the data we obtained from observation. Based on the model we can conclude whether the parking system of Mall Ambassador is already optimum and met the criteria that had given previously. Objectives
  • 9. 9
  • 11. 11 Determining data requirement Source of data Analyze the data using software Step of Data Collection
  • 12. 12 Arrival time Quantity of parking areaService time Arrival rate Pattern of parking area Distribution Number of servers Duration time at the Mall Data Requirement Searching time Working hour Peak hour What do we need to do simulation?
  • 13. 13 System Documentation We made a documentation of layout and quantity parking area Personal Observation We did personal observation such as direct observation and made a questionnaire Personal Interviews We interviewed Mr. Zaeni as Head of Parking Area at Ambassador Mall Resource of Data Where are the data come from?
  • 14. 14 Mall Ambassador Jakarta Place 26 September 2015 Date 10.00 am – 10.00 pm Time Observation
  • 15. Result of Data Observation 15
  • 16. 16 Data Processing in Observation We observed from 10.00 am until 22.00 (12 hours), then we processed data to determine peak hours in Ambassador Mall. The result is from 11.00-17.00 is peak hours in Ambassador Mall 131 228 255 195 158 187 144 127 83 79 66 35 0 50 100 150 200 250 300 Number  of  Car Arrival  Rate
  • 17. Data Processing in Observation 228 255 195 158 187 144 0 50 100 150 200 250 300 Arrival  Rate  in  Peak  Hour   avg  arrival   rate 7.20 5.40 5.51 6.24 5.44 5.84 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 Service  Time  in  Peak  Hour avg  service   time   15.79 14.07 18.44 22.76 19.04 24.95 0.00 5.00 10.00 15.00 20.00 25.00 30.00 Inter  Arrival  Time  in  Peak  Hour avg  inter   arrival  time   17
  • 19. 19 Data Analysis in Observation Arrival Rate Service Time
  • 20. 20 Data Analysis in Observation Searching Time Inter Arrival Time
  • 21. 21 Questionnaire The questionnaire consists of: 01 How often do they go to Mall Ambassador 02 Time interval in entering the Mall Ambassador 03Duration of parking time in Mall Ambassador 04 The length of waiting time in queue
  • 22. 22 Result of Questionnaire 20 44 21 8 0 5 10 15 20 25 30 35 40 45 50 10.00-­‐12.00 12.00-­‐15.00 15.00-­‐18.00 18.00-­‐21.00 Arrival  TimeQuestionnaire Arrival  Time 10.00-­‐12.00 20 12.00-­‐15.00 44 15.00-­‐18.00 21 18.00-­‐21.00 8 Time  Duration 1-­‐2  jam 27 2-­‐3  jam 50 >3  jam 16 27 50 16 0 10 20 30 40 50 60 1-­‐2  jam 2-­‐3  jam >3  jam Time  Duration
  • 23. 23 Data Analysis in Questionnaire This distribution shows the distribution of data from questionnaire is normal distribution.
  • 27. 27 Model Construction Put a layout as background for the model Build location and Location Logic for the model Build Entities and Entities Logic for the model Build Process and Process Logic for the model Build Arrivals and Arrival Logic for the model 1 2 3 4 5 Run the model 6 Entrance Queue Locket Parking Area Exit
  • 28. Put a layout as background for the model 28
  • 29. Build Location and Location Logic for the model 29
  • 30. 30 Build Location and Location Logic for the model
  • 31. Build Entities and Entities Logic for the model 31
  • 32. Build Process and Process Logic for the model 32
  • 33. Build Arrivals and Arrivals Logic for the model 33
  • 34. Run the model 34 Click to watch the video of model simulation
  • 36. 36 01 Watching the animation 02 Comparing with other models 03Conducting degeneracy and extreme condition test Validation We use these following techniques for validating the model: 04 Performing sensitivity analysis 05Running trace
  • 37. 37 Watching the Animation After the model was done, we have to run the model to see whether the model is correct or not.
  • 38. Comparing with Other Models We have to make sure the model is correct by comparing with the excel data. After we run the model, we can see the statistic, if the number is the same with the excel calculating, then our model is finally correct 38
  • 39. 39
  • 40. 40 Conducting Degeneracy and Extreme Condition Test In conducting degeneracy and extreme condition test we can change the arrival rate. Assume that we change the arrival rate to 0 (zero) following with the accuracy, then the result should be: No car arrive in the entry queue. If it is happened, then our model is finally correct
  • 41. In performing sensitivity analysis, we can try to change the service time, if we change into the smaller number of service time there will be no queue, if we change into bigger number there will be queue. After we try, our model adjust with the changing of numbers, it means the model is correct Performing Sensitivity Analysis 41
  • 42. 42 Running Trace Running trace will show all of the event on the discrete model. There is no error on our model, based on the trace results
  • 43. 43 01 Reviewing model code 02 Checking for reasonable input and output 03Watching the animation Verification In order to verified the model, we use some techniques: 04 Using trace and debugging facilities
  • 45. Checking for Reasonable Input and Output 45 The number of entry car is same as the number of exit car and the number of car at the current location, so input and output are reasonable.
  • 46. Watching the Animation We can know that the model is correct if the model is running until it’s done correctly and without bug. 46
  • 47. 47 Using Trace and Debugging Facilities We use trace and debugging facilities to make sure we build the model correctly implemented with good input and structure. There is no error on our model, based on the trace results. There is no debugging in our model.
  • 49. 49 01 The peak time is at 11.00-17.00 02 -Arrival Rate at peak Time: P(195;41.8) sec -Service Time at peak Time: L(5.94;0.69) sec 03Average waiting time in queue: 0.15 sec Results Our Result is: 04 Average cars in queue: 0.01 05 The data from spreadsheet is same with the data from promodel
  • 50. 50 01 The capacity should be increased by 25 parking slot to meet the requirement. 02 The number of ticket locket is enough to meet the requirement. Recommendation Our Recommendation is: