contoh desain slide presentasi ilmiah kreatif dan menarik #3

QUEUEING MODEL KOTA KASABLANKA MALL PARKING SPACES

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

OUTLINE OF PRESENTATION

Determine

the Problem Model

Construction

Data

Collection and

Analysis

Model

Conceptualization

Conclusion and

Suggestion

Validation &

Verification

1 6 5 4 3 2

Determine

The Problem

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.

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 time

4.

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

Model

Conceptualization

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

Data Collection

And Analysis

Data Collection

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

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

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

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

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

Data Analysis

DATA COLLECTION & ANALYSIS Identify distribustion of each type data

1 Service Time

Server 1

2 Service Time

Server 2

Log Logistic

Distribution

DATA COLLECTION & ANALYSIS Identify distribustion of each type data

3 Arrival Rate

Server 1

4 Arrival Rate

Server 2

Poisson

DATA COLLECTION & ANALYSIS Identify distribustion of each type data

5 Long Time

Parking Normal

distribution

Model

Construction

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

Locations 1

MODEL CONSTRUCTION To make a model, we should at least have 4 basic Elements (cont’d)

Entities, only consist of 1 entity, the customer itself

2

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 gate

3

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

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 simulation 1 2

Validation &

Verification

Validation

Model Conceptualization Validation

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 Validity 1 2

Model Conceptualization Validation

Face Validity

Tracing the truth of the model logic and

computer model (debugging)

VALIDATION & VERIFICATION

Trace Validity Result

1

Model Conceptualization Validation

VALID

VALIDATION & VERIFICATION

Asked one of the Kota

Kasablanka’s Parking

Supervisor called Mr. Tri

Direct observation on the field

Model Conceptualization Validation

Face Validity 2

VALID

Validation

Model Validation

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

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 1 1a

Model Validation

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 2 1b

Watching the Animation

2

VALIDATION & VERIFICATION Model Validation

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

Running Traces

VALIDATION & VERIFICATION Model Validation

4

VALIDATION & VERIFICATION

VALID

Through model conceptulization

validation and model

validation, we know that the

model is valid

Veficiation

Model Verfication

VALIDATION & VERIFICATION Model Verification

Visual verification whether the

model running has been right

Checking for code errors or

inconsistency

1 2 3 Watching 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

Watching the Animation

1

VALIDATION & VERIFICATION Model Verification

VALIDATION & VERIFICATION Model Verification

Reviewing Model Code

2

VALIDATION & VERIFICATION Model Verification

Reviewing Model Code (cont’d)

2

VALIDATION & VERIFICATION Model Verification

Reviewing Model Code (cont’d)

2

VALIDATION & VERIFICATION Model Verification

Using Traces and Debugging Facilities 2

There are no bugs, so

the model can run

perfectly

VALIDATION & VERIFICATION

VERIFIED

Through model verification, we

know that the model is

verified and can run properly

Output Analysis,

Conclusion

&Suggestion,

Output Analysis

Data at per Locations

OUTPUT ANALYSIS

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

Location states multi

OUTPUT ANALYSIS

Location states single

OUTPUT ANALYSIS

OUTPUT ANALYSIS

Conclusion and Suggestion

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

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

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

THANKYOU