Chapter 13 Simulation Modeling

1) Simulation of a business or process is
generally performed by building a mathematical model to represent the process
or system.

2) Simulation models are designed to
generate optimal solutions, which can then be applied to real-world situations.

3) A major advantage of using simulation
techniques is to be able to study the interactive effect of individual
components/variables.
4) Despite the power of simulation, less
than 20% of the largest U.S. corporations use simulation in corporate planning.
5) One of the major advantages of
simulation is “time compression,” i.e., the ability to study in a
relatively short period, activities that would, in reality, take place over a
period of days, months, or even years.

6) To “simulate” is to try to
duplicate the features, appearance, and characteristics of a real system.
7) While it is powerful, simulation is not
considered to be a flexible quantitative analysis tool.

8) Simulation can use any probability
distribution that the user defines; it does not require standard distributions.
9) One disadvantage of simulation is that
it does not allow for “what-if?” types of questions.

10) Simulation models may contain both
deterministic and probabilistic variables.
11) The Monte Carlo simulation was
developed as a quantitative technique by the great mathematician John von
Neumann during World War I.
12) Simulation models are limited to using
standard probability distributions such as Poisson, exponential, normal, etc.

13) The Monte Carlo simulation is used
with variables that are probabilistic.
14) The probability of selecting any
random number in a two digit table is 1/100.

15) When using a random number generator,
one should never start in the middle of the table of random numbers.
16) If we are using a Monte Carlo
simulation model, we should expect the model to produce the same results for
each set of random numbers used.

17) The four disadvantages of simulation
are cost, its trial-and-error nature, time compression, and uniqueness.

18) The wider the variation among results
produced by using different sets of random numbers, the longer we need to run
the simulation to obtain reliable results.

19) Simulation is very flexible. Thus, its
solutions and inferences are usually transferable to other problems.

20) Simulation models are useful for
economic order quantity problems with probabilistic demand and lead time.

21) Deterministic inventory models require
the use of simulation.
22) A flow diagram is helpful in the
logical coding procedures for programming a simulation process.
23) If, in a simple queuing or waiting
line problem, we wish to know the maximum likely waiting time, or the maximum
likely length of the line, we must use a simulation model.

24) If, for a simple queuing or waiting
line problem, we compare the solution from an analytical model with that from a
simulation, we will typically find them to be exactly the same.

25) The advantage of simulation over
queuing or waiting line models is that simulation allows us to relax our
assumptions regarding arrival and service distributions.

26) Simulation that is used with queuing
models are probabilistic and do not have Poisson arrivals nor exponential
service times.
27) Simulation of maintenance problems can
help management analyze various staffing strategies based on machine downtime
and labor cost.

28) When establishing a probability
distribution based on historical outcomes, the relative frequency for each
possible outcome of a variable is found by dividing the frequency of each
outcome by the total number of observations.

29) Operational gaming involves a single
player competing with the computer simulated game.

30)
There are three categories of simulation models: Monte Carlo, operational
gaming, and systems simulation