Operations management by stevenson 9th ch18s

Learning Objectives Explain what is meant by the term simulation  List some of the reasons for simulation’s popularity as a tool for decision making  Explain how and why random number

Simulation

Learning Objectives

 Explain what is meant by the term simulation

 List some of the reasons for simulation’s popularity

as a tool for decision making

 Explain how and why random numbers are used

are used in simulation

 Outline the advantages and limitations of simulation

 Describe the alternatives that a manager would

reject before choosing simulation as a decision

making tool

 Solve typical problems that require simulation

Simulation: 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

Simulation Process

1 Identify the problem

2 Develop the simulation model

3 Test the model

4 Develop the experiments

5 Run the simulation and evaluate results

6 Repeat 4 and 5 until results are

satisfactory

Monte Carlo Simulation

Monte 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

Example S-1

Example S-1

Simulating Distributions

 Poisson

 Mean of distribution is required

 Normal

 Need to know the mean and standard

deviation

Simulated

Random number

Standard deviation

=

Uniform Distribution

F(x)

Simulated

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

Figure 18S.1

Negative Exponential Distribution

Figure 18S.2

F(t)

P t( ≥ T) = RN

Computer Simulation

 Simulation languages

 SIMSCRIPT II.5

 GPSS/H

 GPSS/PC

 RESQ

Advantages of Simulation

mathematically

show long-term effects

for decision makers

Limitations of Simulation

random systems