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
