PARKING SYSTEM MODELLINGof Mall Ambassador

Group 7Anindya 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

�

Capacity360 parking spots.

Parking LotHas 3 basement levels of

parking lot.

LocationJl. Prof. Dr. Satrio No. 14, Kuningan, Jakarta Selatan, Banten 15810, Indonesia

Working Hours10.00 – 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 Repor t and Presentation

6

�

�

�

�

Problem Formulation

6

�

�

�

�

With such high visitors coming to MallAmbassador and many of them drive cars,this Mall only provides small spaces forparking – only 360 parking spots available– causing a queue when entering the parkingspot and difficulties in finding the parkingspot, especially on peak time.

Problem Statement

�

�

�

�

7

Make the model based on the data weobtained from observation. Based on themodel we can conclude whether theparking system of Mall Ambassador isalready optimum and met the criteria thathad 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 DataWhere are the data come from?

14

�

�

�

�

Mall Ambassador Jakarta

Place26 September 2015

Date10.00 am – 10.00 pm

Time

�

Observation

�

Result of Data Observation15

�

�

�

�

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

228255

195158

187

144127

83 79 6635

0

50

100

150

200

250

300

Number  of  Car

Arrival  Rate

Data Processing in Observation

228255

195

158 187144

050

100150200250300

Arrival  Rate  in  Peak  Hour  

avg  arrival  rate

7.20

5.40 5.51

6.245.44

5.84

0.001.002.003.004.005.006.007.008.00

Service  Time  in  Peak  Hour

avg  service  time  

15.79

14.07

18.4422.76

19.04

24.95

0.005.00

10.0015.0020.0025.0030.00

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

03 Duration of parking time in Mall Ambassador

04 The length of waiting time in queue

22

�

�

�

�

Result of Questionnaire

20

44

21

8

05101520253035404550

10.00-­‐12.00 12.00-­‐15.00 15.00-­‐18.00 18.00-­‐21.00

Arrival  TimeQuestionnaire

Arrival  Time10.00-­‐12.00 2012.00-­‐15.00 4415.00-­‐18.00 2118.00-­‐21.00 8

Time  Duration1-­‐2  jam 272-­‐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

�

�

�

�

EntranceQueueLocket

Parking AreaExit

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

03 Conducting degeneracy and extreme condition test

��

ValidationWe use these following techniques for validating the model:

04 Performing sensitivity analysis

05 Running 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 bycomparing with the excel data. After we run the model,we can see the statistic, if the number is the same withthe excel calculating, then our model is finally correct

38

�

�

�

�

39

�

�

�

�

40Conducting 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 Analysis41

�

�

�

�

�

� �

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

03 Watching the animation��

Verification

In order to verified the model, we use some techniques:

04 Using trace and debugging facilities

Reviewing Model Code44

�

�

�

�

Checking for Reasonable Input and Output45

�

�

�

�

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

03 Average waiting time in queue: 0.15 sec

��

ResultsOur 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.

��

RecommendationOur Recommendation is:

THANK YOU!