Introduction to Simulation. What is simulation? A simulation is the imitation of the operation of a real-world system over time. It involves the generation.

Slides:



Advertisements
Similar presentations
McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. A PowerPoint Presentation Package to Accompany Applied Statistics.
Advertisements

What is Chi-Square? Used to examine differences in the distributions of nominal data A mathematical comparison between expected frequencies and observed.
1 Overview of Simulation When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions set for analytic.
11 Simulation. 22 Overview of Simulation – When do we prefer to develop simulation model over an analytic model? When not all the underlying assumptions.
Hypothesis testing and confidence intervals by resampling by J. Kárász.
Monte Carlo Simulation A technique that helps modelers examine the consequences of continuous risk Most risks in real world generate hundreds of possible.
Simulation Operations -- Prof. Juran.
Session 7a. Decision Models -- Prof. Juran2 Overview Monte Carlo Simulation –Basic concepts and history Excel Tricks –RAND(), IF, Boolean Crystal Ball.
(Monté Carlo) Simulation
Outline/Coverage Terms for reference Introduction
Spreadsheet Simulation
Spreadsheet Demonstration
Module F: Simulation. Introduction What: Simulation Where: To duplicate the features, appearance, and characteristics of a real system Why: To estimate.
Fall 2006 – Fundamentals of Business Statistics 1 Chapter 6 Introduction to Sampling Distributions.
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 15-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 15.
Example 11.1 Simulation with Built-In Excel Tools.
Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.
Simulation Basic Concepts. NEED FOR SIMULATION Mathematical models we have studied thus far have “closed form” solutions –Obtained from formulas -- forecasting,
QMF Simulation. Outline What is Simulation What is Simulation Advantages and Disadvantages of Simulation Advantages and Disadvantages of Simulation Monte.
1 1 Slide Chapter 6 Simulation n Advantages and Disadvantages of Using Simulation n Modeling n Random Variables and Pseudo-Random Numbers n Time Increments.
Monté Carlo Simulation MGS 3100 – Chapter 9. Simulation Defined A computer-based model used to run experiments on a real system.  Typically done on a.
Simulation.
Computer Simulation A Laboratory to Evaluate “What-if” Questions.
Monte Carlo SIMULATION with EXCEL Montana Going Green Workshop October 2010.
Chapter 9: Simulation Spreadsheet-Based Decision Support Systems Prof. Name Position (123) University Name.
Example 16.1 Ordering calendars at Walton Bookstore
Managerial Decision Modeling with Spreadsheets
Continuous Random Variables
Population All members of a set which have a given characteristic. Population Data Data associated with a certain population. Population Parameter A measure.
Copyright ©2011 Nelson Education Limited The Normal Probability Distribution CHAPTER 6.
ESD.70J Engineering Economy Module - Session 21 ESD.70J Engineering Economy Module Fall 2005 Session One Alex Fadeev - Link for this PPT:
Contemporary Engineering Economics, 6 th edition Park Copyright © 2016 by Pearson Education, Inc. All Rights Reserved Risk Simulation Lecture No. 40 Chapter.
Discrete Distributions The values generated for a random variable must be from a finite distinct set of individual values. For example, based on past observations,
Modular 11 Ch 7.1 to 7.2 Part I. Ch 7.1 Uniform and Normal Distribution Recall: Discrete random variable probability distribution For a continued random.
Chapter 4 MODELING AND ANALYSIS. Model component Data component provides input data User interface displays solution It is the model component of a DSS.
Crystal Ball: Risk Analysis  Risk analysis uses analytical decision models or Monte Carlo simulation models based on the probability distributions to.
Outline of Chapter 9: Using Simulation to Solve Decision Problems Real world decisions are often too complex to be analyzed effectively using influence.
Simulation OPIM 310-Lecture #4 Instructor: Jose Cruz.
Simulation is the process of studying the behavior of a real system by using a model that replicates the behavior of the system under different scenarios.
ESD.70J Engineering Economy Module - Session 21 ESD.70J Engineering Economy Fall 2006 Session Two Alex Fadeev - Link for this PPT:
Introduction to Inferential Statistics Statistical analyses are initially divided into: Descriptive Statistics or Inferential Statistics. Descriptive Statistics.
Determination of Sample Size: A Review of Statistical Theory
Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.
Simulation Using computers to simulate real- world observations.
Monte Carlo Process Risk Analysis for Water Resources Planning and Management Institute for Water Resources 2008.
T06-02.S - 1 T06-02.S Standard Normal Distribution Graphical Purpose Allows the analyst to analyze the Standard Normal Probability Distribution. Probability.
Simulation is the process of studying the behavior of a real system by using a model that replicates the system under different scenarios. A simulation.
Risk Analysis Simulate a scenario of possible input values that could occur and observe key impacts Pick many input scenarios according to their likelihood.
Computer Simulation. The Essence of Computer Simulation A stochastic system is a system that evolves over time according to one or more probability distributions.
1 BA 555 Practical Business Analysis Linear Programming (LP) Sensitivity Analysis Simulation Agenda.
Risk Analysis Simulate a scenario of possible input values that could occur and observe key financial impacts Pick many different input scenarios according.
ESD.70J Engineering Economy Module - Session 21 ESD.70J Engineering Economy Fall 2010 Session Two Xin Zhang – Prof. Richard de Neufville.
MONTE CARLO ANALYSIS When a system contains elements that exhibit chance in their behavior, the Monte Carlo method of simulation may be applied.
Simulation Chapter 16 of Quantitative Methods for Business, by Anderson, Sweeney and Williams Read sections 16.1, 16.2, 16.3, 16.4, and Appendix 16.1.
MAT 4830 Mathematical Modeling 04 Monte Carlo Integrations
Simulation Discrete Variables. What is it? A mathematical model Probabilistic Uses the entire range of possible values of a variable in the model.
Continuous Random Variables Lecture 24 Section Tue, Oct 18, 2005.
Cell Diameters and Normal Distribution. Frequency Distributions a frequency distribution is an arrangement of the values that one or more variables take.
Simulations and Normal Distribution Week 4. Simulations Probability Exploration Tool.
AGB 260: Agribusiness Information Technology Business Modeling and Analysis.
AGB 260: Agribusiness Data Literacy
Computer Simulation Henry C. Co Technology and Operations Management,
Prepared by Lloyd R. Jaisingh
Monte Carlo Simulation
Monte Carlo Simulation
Introduction to Inferential Statistics
AP Statistics: Chapter 7
Simulation Modeling.
Simulation Discrete Variables.
Chapter 5 Normal Probability Distributions.
Presentation transcript:

Introduction to Simulation

What is simulation? A simulation is the imitation of the operation of a real-world system over time. It involves the generation of an artificial history of a system. The observations of the artificial history are used to draw inferences about the operating characteristics of the system.

Simulation Models A simulation model consists of a set of assumptions that describe the operation of a system. These assumptions are expressed in mathematical, logical, and symbolic relationships between the entities of the system.

Simulation Models The simulation model, once developed and validated, can be used to investigate a wide variety of “what-if” questions about the real- world system.

Monte Carlo Simulation

A Manual Algorithm 1.Calculate the relative frequency of occurrence of each possible outcome  relative frequency = number of times x occurs / total number of observations = Pr(outcome = x)

Spare Parts Example

A Manual Algorithm 2.Calculate the cumulative distribution of the possible outcomes - Pr(outcome <= x)

Spare Parts Example

A Manual Algorithm 3.Use random numbers to simulate the possible outcomes by associating these numbers with the intervals of the cumulative distribution.  random numbers = a set of numbers, each of which has the same probability of occurring

Spare Parts Example

A Manual Algorithm 4.Repeat step 3 a suitable number of times to generate the desired statistics.

Spare Parts Example

Simulation with Spreadsheets

Random Variable Generation RAND() - this function returns a uniformly distributed random number between 0.0 and 1.0. NORMINV(RAND,  ) - this function returns a normally distributed random variable with mean=  and standard deviation =  Some other distributions can be generated by formula.

Empirical Distributions Empirical distributions are distributions based on observed historical data that are not fit to any specific probability distribution.  Embedded ifs IF(logical_test,value_if_true,value_if_false)  Table lookups VLOOKUP

Replicating the Model Data Tables To set up a two-input data table  In a cell, enter the formula that will use the substituted values.  Starting in the cell below the formula, enter the values that you want substituted into one input cell. Enter these values in the same column as the formula.

Replicating the Model  Starting in the cell to the right of the formula, enter the values that you want substituted into the other input cell. Enter these values in the same row as the formula.

Data Analysis Descriptive Statistics Histogram Anova Etc.