Session #11 Agenda
Other High Level Simulation Languages
Verification , Validation, Accreditation (VVA)
Application Specific Languages
Agent Based Modeling Netlogo Ex.
Final Project Q&A
Simulation Languages
. Simulink MatLab
Modelica
PSM+ - QMS
Desire
Introduction to Probability and Statistics
Part II
Lesson Objectives
Lesson Objectives
Role of Continuous Process Generators
Inverse Transform Method
Lots Problems
Next Lesson
Building Systems Simulation Models
Banking Example
Some Special Distributi
Forecast: E1
1,000 Trials
Frequency Chart
992 Displayed
.017
17
.013
12.75
.009
8.5
.004
4.25
.000
Random Numbers
0
0.45
0.47
0.50
0.52
0.55
f(x)
1
b-a
a
b
b
f(x)dx =
a
b-a =
1
b-a
x
Lesson Objectives
Lesson Objectives
Random Number Generation
Next Lesso
Queuing
Queuing theory is the mathematical
study of waiting lines (or queues)
Example of Queuing Systems
SYSTEM
Reception Desk
Repair Facility
Garage
Tool Crib
Hospital
Warehouse
Airport
Production Line
Road Network
Market
Laundry
Job Shop
Lumberyard
Saw
Introduction to Probability and Statistics
Part II
Lesson Objectives
Lesson Objectives
Role of Continuous Process Generators
Inverse Transform Method
Lots Problems
Next Lesson
Building Systems Simulation Models
Banking Example
Some Special Distributi
Queuing
Queuing theory is the mathematical
study of waiting lines (or queues)
Example of Queuing Systems
SYSTEM
Reception Desk
Repair Facility
Garage
Tool Crib
Hospital
Warehouse
Airport
Production Line
Road Network
Market
Laundry
Job Shop
Lumberyard
Saw
Simulation
in Practice
Lesson Objectives
Lesson Objectives
Some Simple Practical Examples
Next Lesson
Role of Probability and Statistics I
Class Problem 4 - 2
The manager of Cafe Java is trying to determine whether to hire another cashier for the mornin
Forecast: E1
1,000 Trials
Frequency Chart
992 Displayed
.017
17
.013
12.75
.009
8.5
.004
Random Numbers
4.25
.000
0
0.45
0.47
0.50
0.52
0.55
f(x)
1
b-a
a
b
b
f(x)dx =
a
b-a =
1
b-a
x
Lesson Objectives
Lesson Objectives
Random Number Generation
Next Lesso
Simulation
in Practice
Lesson Objectives
Lesson Objectives
Some Simple Practical Examples
Next Lesson
Role of Probability and Statistics I
Class Problem 4 - 2
The manager of Cafe Java is trying to determine whether to hire another cashier for the mornin
Simulation Using Spreadsheets
Lesson Objectives
Lesson Objectives
Simulations Using Spreadsheets
Modeling Building Process
Special Functions
Introduction to Crystal Ball
Next Lesson
More Probability and Statistics
Why and Why Not Spreadsheets?
Why Sp
Simulation Using Spreadsheets
Lesson Objectives
Lesson Objectives
Simulations Using Spreadsheets
Modeling Building Process
Special Functions
Introduction to Crystal Ball
Next Lesson
More Probability and Statistics
Why and Why Not Spreadsheets?
Why Sp
Social Network Analysis (SNA)
including a tutorial on concepts and methods
Social Media Dr. Giorgos Cheliotis ([email protected])
Communications and New Media, National University of Singapore
Background: Network Analysis
SNA has its origins in both s
Problem
1.1
A) productivity=units
produced/labor-hour
used=120boxes/40hours=3units/labor-hour
B) produced/labor-hour
used=125boxes/40hours=3.125units/labor-
hour
C) Unit increase=3.125-3=0.125units/labor-hour
D) Percentage change=(0.125/3)*100%=4.166%
1.2
Introduction to
Probability and Statistics
Part I
f(x)
1
b-a
a
b
b
f(x)dx =
a
f(x)
0.4
0.3
N(0,1)
0.2
0.1
0
-4
-2
0
x
2
4
b-a =
1
b-a
x
Lesson Objectives
Lesson Objectives
Random Number Generation
Role of Continuous Process Generators
Inverse Transform
Introduction to
Probability and Statistics
Part I
f(x)
1
b-a
a
b
b
f(x)dx =
a
f(x)
0.4
0.3
N(0,1)
0.2
0.1
0
-4
-2
0
x
2
4
b-a =
1
b-a
x
Lesson Objectives
Lesson Objectives
Random Number Generation
Role of Continuous Process Generators
Inverse Transform
N(0,1)
t10
t5
1 (
2
(
3
4 (
.
.
100 (
Output Analysis
0
)
)
(
)
)
)
Lesson Objectives
Lesson Objectives
Confidence Intervals
Brief Overview of P Values
Next Lesson
Communication
Miscellaneous Definitions
x = mean of the sample
= mean of the population
Introduction to Agent-based
Modeling and Simulation
Charles M. Macal and Michael J. North
Center for Complex Adaptive Agent Systems Simulation
(CAS2),
Decision & Information Sciences Division,
Argonne National Laboratory, Argonne, IL 60439 USA
[email protected]
Module 2
Systems Engineering,
Bounding Project Scope
Systems Engineering, Requirements, Project Charter
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Defining the Project
Systems Engineering
Bounding the Project Scope and Requi
Module 3 - Work Breakdown
Structure
Planning Process, Work Breakdown Structure
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Learning Objectives
At the end of this module, you will be able to:
Understand the steps in the project
Module 4
Organizing for PM,
Organizational Structures
Organization Functional, Project, Matrix; PMO
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Project Organization
There are 3 basic types of organization in project
management
A Garbage Can Model of Organizational Choice
Author(s): Michael D. Cohen, James G. March and Johan P. Olsen
Source: Administrative Science Quarterly, Vol. 17, No. 1 (Mar., 1972), pp. 1-25
Published by: Sage Publications, Inc. on behalf of the Johnson Grad
Module 6 Estimating
and Budgeting
Estimating, Budgeting, Top-down, Bottom-up
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Topic Objectives
After completing this topic, you should be able to
Recognize the basic estimation techni
Module 8
Risk and Opportunity
Management
Risk and Opportunity Management, Probability, Consequences,
Mitigation, Risk Log
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Risk Management
At the end of this module, you will be able t
Module 9
Contracting and
Sub-contracting
Contracts, Subcontracts, Contract Types
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Learning Objectives
At the end of this module, you will be able to:
Understand the importance of a c
Module 10
Project Assessment and
Control
Assessment, Control, Earned Value
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Learning Objectives
At the end of this module, you will be able to:
Understand the elements of project cont
Module 12
Project Quality Management
Quality Planning, Quality Control, Metrics, Tools, Quality Processes
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Learning Objectives
At the end of this module, you will be able to:
Why Qual
Module 14
Ethics in Project
Management
Ethics, Morality, Conduct
Copyright 2011 Stevens Institute of Technology. All rights reserved.
1
Learning Objectives
At the end of this module, you will be able to:
Describe the importance of ethics in the Project
N(0,1)
t10
t5
1 (
2
(
3
4 (
.
.
100 (
Output Analysis
0
)
)
(
)
)
)
Lesson Objectives
Lesson Objectives
Confidence Intervals
Brief Overview of P Values
Next Lesson
Communication
Miscellaneous Definitions
x = mean of the sample
= mean of the population
Problem
1.1
A) productivity=units
produced/labor-hour
used=120boxes/40hours=3units/labor-hour
B) produced/labor-hour
used=125boxes/40hours=3.125units/labor-hour
C) Unit increase=3.125-3=0.125units/labor-hour
D) Percentage change=(0.125/3)*100%=4.166%
1.2