supplement to chapter nineteen irwin/mcgraw-hill © the mcgraw-hill companies, inc., 1999 simulation...

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SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

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Page 1: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-1

Chapter 19 Supplement

Simulation

Page 2: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-2

SimulationSimulation: a descriptive technique that enables a decision maker to evaluate the behavior of a model under various conditions.

•Simulation models complex situations

•Models are simple to use and understand

•Models can play “what if” experiments

•Extensive software packages available

Page 3: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-3

Simulation Process

• Identify the problem• Develop the simulation model• Test the model• Develop the experiments• Run the simulation and evaluate results• Repeat until results are satisfactory

Page 4: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-4

Monte Carlo SimulationMonte Carlo method: Probabilistic simulation technique used when a process has a random component

•Identify a probability distribution

•Setup intervals of random numbers to match probability distribution

•Obtain the random numbers

•Interpret the results

Page 5: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-5

Simulating Distributions• Poisson

– Mean of distribution is required

• Normal– Need to know the mean and standard

deviation

Simulatedvalue

Mean Randomnumber

Standarddeviation

+ X=

Page 6: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-6

Uniform Distribution

a b0 x

F(x)

Simulatedvalue

a + (b - a)(Random number as a percentage)=

Figure 19S-1

Page 7: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-7

Negative Exponential Distribution

F(t)

0 T t

P t T RN( ) .

Figure 19S-2

Page 8: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-8

Advantages of Simulation

• Solves problems that are difficult or impossible to solve mathematically

• Allows experimentation without risk to actual system

• Compresses time to show long-term effects

• Serves as training tool for decision makers

Page 9: SUPPLEMENT TO CHAPTER NINETEEN Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., 1999 SIMULATION 19S-1 Chapter 19 Supplement Simulation

SUPPLEMENT TO CHAPTER NINETEEN

Irwin/McGraw-Hill ©The McGraw-Hill Companies, Inc., 1999

SIMULATION

19S-9

Limitations of Simulation

• Does not produce optimum solution

• Model development may be difficult

• Computer run time may be substantial

• Monte Carlo simulation only applicable to random systems