Module F: Simulation. Introduction What: Simulation Where: To duplicate the features, appearance, and characteristics of a real system Why: To estimate.

Slides:



Advertisements
Similar presentations
Chapter 15: Quantitatve Methods in Health Care Management Yasar A. Ozcan 1 Chapter 15. Simulation.
Advertisements

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.
Model Simulasi Pertemuan 22 Matakuliah: K0414 / Riset Operasi Bisnis dan Industri Tahun: 2008 / 2009.
Chapter 10: Simulation Modeling
CS433 Modeling and Simulation Lecture 11 Monté Carlo Simulation Dr. Anis Koubâa 05 Jan 2009 Al-Imam Mohammad Ibn.
MGT 560 Queuing System Simulation Stochastic Modeling © Victor E. Sower, Ph.D., C.Q.E
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
Simulation Modeling Chapter 14
© 2006 Prentice Hall, Inc.F – 1 Operations Management Module F – Simulation © 2006 Prentice Hall, Inc. PowerPoint presentation to accompany Heizer/Render.
Steps of a sound simulation study
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 15-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 15.
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.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J F-1 Operations Management Simulation Module F.
Chapter 14 Simulation. Monte Carlo Process Statistical Analysis of Simulation Results Verification of the Simulation Model Computer Simulation with Excel.
Lecture 1 Introduction to Simulation. 2 The Opportunity Game Cost to Play: $1000 Payoff ($): (A Spinner) x (B Spinner) – (C Spinner)
SIMULATION. Simulation Definition of Simulation Simulation Methodology Proposing a New Experiment Considerations When Using Computer Models Types of Simulations.
Descriptive Modelling: Simulation “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose.
Robert M. Saltzman © DS 851: 4 Main Components 1.Applications The more you see, the better 2.Probability & Statistics Computer does most of the work.
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.
Operations Management
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
Introduction to ModelingMonte Carlo Simulation Expensive Not always practical Time consuming Impossible for all situations Can be complex Cons Pros Experience.
SIMULATION An attempt to duplicate the features, appearance, and characteristics of a real system Applied Management Science for Decision Making, 1e ©
Managerial Decision Modeling with Spreadsheets
Chapter 1 Introduction to Simulation
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
Simulation Prepared by Amani Salah AL-Saigaly Supervised by Dr. Sana’a Wafa Al-Sayegh University of Palestine.
© 2007 Pearson Education Simulation Supplement B.
F Simulation PowerPoint presentation to accompany Heizer and Render
B – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Simulation Supplement B.
F - 1© 2011 Pearson Education, Inc. publishing as Prentice Hall F F Simulation PowerPoint presentation to accompany Heizer and Render Operations Management,
1 1 Slide Simulation. 2 2 Simulation n Advantages and Disadvantages of Simulation n Simulation Modeling n Random Variables n Simulation Languages n Validation.
Module 1: Statistical Issues in Micro simulation Paul Sousa.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 1 Simulation.
SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation.
Areas Of Simulation Application
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.
Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc.,
Monté Carlo Simulation  Understand the concept of Monté Carlo Simulation  Learn how to use Monté Carlo Simulation to make good decisions  Learn how.
McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 1.
FIN 614: Financial Management Larry Schrenk, Instructor.
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J F-1 Operations.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Simulation. Introduction What is Simulation? –Try to duplicate features, appearance, and characteristics of real system. Idea behind Simulation –Imitate.
Operations Research The OR Process. What is OR? It is a Process It assists Decision Makers It has a set of Tools It is applicable in many Situations.
Chapter 19 Monte Carlo Valuation. Copyright © 2006 Pearson Addison-Wesley. All rights reserved Monte Carlo Valuation Simulation of future stock.
1 1 Slide Simulation Professor Ahmadi. 2 2 Slide Simulation Chapter Outline n Computer Simulation n Simulation Modeling n Random Variables and Pseudo-Random.
Operations Management
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.
Simulation in Healthcare Ozcan: Chapter 15 ISE 491 Fall 2009 Dr. Burtner.
Simulation Sesi 12 Dosen Pembina: Danang Junaedi IF-UTAMA1.
MAT 4830 Mathematical Modeling 04 Monte Carlo Integrations
EMIS 7300 SYSTEMS ANALYSIS METHODS FALL 2005 Dr. John Lipp Copyright © 2005 Dr. John Lipp.
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 15-1 © 2006 by Prentice Hall, Inc. Upper Saddle River, NJ Prepared by.
Simulation Modeling.
Simulasi sistem persediaan
Computer Simulation Henry C. Co Technology and Operations Management,
Prepared by Lee Revere and John Large
Professor S K Dubey,VSM Amity School of Business
Simulation Modeling.
Simulation Modeling Chapter 15
Simulation Modeling Chapter 15
Simulation Part 1: Simulation with Discrete Random Variables
Simulation Supplement B.
Presentation transcript:

Module F: Simulation

Introduction What: Simulation Where: To duplicate the features, appearance, and characteristics of a real system Why: To estimate the effects of various actions

Simulation Imitate a real-world situation mathematically Study its properties and operating characteristics Draw conclusions and make decisions based on the results of the simulation

Steps 1. Define problem 2. Introduce variables 3. Construct numerical model 4. Set up possible courses of action 5. Run experiment 6. Consider results 7. Decide course of action

Advantages of Simulation Straightforward and flexible Analyse large and complex real-world systems that cannot be solved by conventional models Real-world complications can be inserted Time compression Allows “what if” questions Does not interfere with real-world system Study interactive effects of individual components

Disadvantages of Simulation Good models are expensive and take months to develop Trial-and-error approach does not generate optimal solutions User must generate all conditions and constraints Each model is unique – solution not transferable to other problems

Monte Carlo Simulation System contains elements that exhibit chance Experiment on chance elements by means of random sampling

Step 1 Establish Probability Distributions

Step 2 Build a cumulative probability distribution for each variable

Step 3 Set random number intervals

Step 4 Generate random numbers (Table F.4)

Step 5 Simulate the experiment

Where can Simulation Be Used? Queueing problems – especially ones that do not follow a Poisson distribution Inventory Analysis – finding order quantity and reorder points where demand and lead time are not constant

Computer Simulations To draw valid results from the simulation, you must repeat it many times The computer makes this easy by generating random numbers, applying them to the model, and tracking the results

Commercial Products Extend – student version on CD SimFactory ModSim MAP/I

Do-It-Yourself With Excel FREQUENCY function VLOOKUP function