Method Of Simulation

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In this article we are going to see about the study simulations topics and problems involving it. The study simulations use the concepts of theoretical and experimental probabilities to solve problems involving uncertainty. Study simulations to do the model of real-world situations involving uncertainty. We can conduct study simulations of the results for many problems of objects we pick should have the same results as the several possible results of the problem, and all outcomes should likely to have equally.

Theoretical and Experimental study simulations:-

Theoretical study simulations of operations are performed mathematically and described what should have happened.

Experimental study simulation is calculated using data from tests or experiments. Experimental study simulation is the ratio of the number of times an outcome happened to the total number of events or trials. This type of ratio is labeled as the relative frequency.

Experimental study simulations = (frequency of an outcome) / (total number of trials).

Empirical study:

It is helpful to do an experiment repeatedly, to ...
... collect the data, and analyze the results. This is recognized as an empirical study.

Performing study simulations:-

Study simulations to compute an experimental chance by using objects of problems to act out an event that would be difficult or impractical to do.

Example 1:

A boy has four balls (2 blue and 2 green balls). What is the most likely mix of blue and green ball?

Assume that P (blue ball) = P (green ball) = 1 / 2.

(a). What objects can be used to simulate the achievable outcomes of the balls?

Each ball can be blue or green, so there are 2 • 2 • 2 • 2 or 16 possible outcomes. Use a simulation that also has 2 outcomes for each of 4 events. One achievable simulation would be to toss four coins, one for each ball, with heads representing blue balls and tails representing green balls.

(b). Find the theoretical chance that there are two green and two blue balls?

There are 16 probable outcomes, and the number of combination that have two green and two blue balls are 4C2 or 6.

So, the theoretical probability is 6 /16 or 3 / 8.

Simulations are techniques for conducting experiments that involve certain types of mathematical and logical relationships necessary to describe the structure and behaviour of a complex real world system. It is a quantitative technique that utilises a computerised mathematical model in order to represent actual decision making under conditions of uncertainty for evaluating alternative courses of action based upon assumptions and facts.

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learn online reasons for applying simulations:

Simulation technique is widely applied to solve Operations research problems. The reasons for using simulation technique are as follows :

Development of analytical solution to the problem may not be possible
The actual observation of the system may be very expensive and time consuming
Simulation may be the only method available where the actual environment is difficult to observe in reality
It may be too disruptive to actually operate and observe the system
There may be a possible time limit to allow the system to operate extensively.
Finally, simulation provides a trial and error movement towards the solution.

Method of simulation - learn online

The simulation process consists of the following steps :

Identify and define the problem clearly
Sort out the objectives of the problem
Construct a suitable mathematical model of the given problem
Make sure that the constructed model represents the real situation of the problem
Obtain a set consistent set of sample values by conducting experiments with the constructed model
Analyze the result obtained by the simulation activity
Repeat the process by making required changes in the model or the sample until solution is obtained for enough number of samples
List the various decision criteria and select the best policy.

Limitations of simulations - learn online

Though there are many advantages in using simulation technique, analysts consider it a method of last resort since there are many limitations for simulation. Some of them are listed below.

Simulation does not produce optimum results
Conducting a number of simulation runs is a time consuming process.
Simulation models are "run" rather than solved.
Even if analytical solutions are better sometimes people develop a tendency of using simulation.
The necessary knowledge of parts of the system does not ensure adequate knowledge of the system's behaviour.

Learn more on about Practice Probability Problems and its Examples. Between, if you have problem on these topics Analyzing Quantitative Data, Please share your comments.