Advantages and disadvantages of simulation are as follows:
Simulation is best suited to analyze complex and large practical problems when it is not possible to solve them through a mathematical method.
Simulation is flexible, hence changes in the system variables can be made to select the best solution among the various alternatives.
In simulation, the experiments are carried out with the model without disturbing the system.
Policy decisions can be made much faster by knowing the options well in advance and by reducing the risk of experimenting in the real system.
Simulation does not generate optimal solutions.
It may take a long time to develop a good simulation model.
In certain cases simulation models can be very expensive.
The decision-maker must provide all information (depending on the model) about the constraints and conditions for examination, as simulation does not give the answers by itself.
Monte Carlo Simulation
In simulation, we have deterministic models and probabilistic models. Deterministic simulation models have the alternatives clearly known in advance and the choice is made by considering the various well-defined alternatives. Probabilistic simulation model is stochastic in nature and all decisions are made under uncertainty. One of the probabilistic simulation models is the Monte Carlo method. In this method, the decision variables are represented by a probabilistic distribution and random samples are drawn from probability distribution using random numbers. The simulation experiment is conducted until the required number of simulations are generated. Finally, the best course of action is selected for implementation. The significance of Monte Carlo Simulation is that decision variables may not explicitly follow any standard probability distribution such as Normal, Poisson, Exponential, etc. The distribution can be obtained by direct observation or from past records.
Procedure for Monte Carlo Simulation
Procedure for Monte Carlo simulation is as follows:
Step 1: Establish a probability distribution for the variables to be analyzed.
Step 2: Find the cumulative probability distribution for each variable.
Step 3: Set Random Number intervals for variables and generate random numbers.
Step 4: Simulate the experiment by selecting random numbers from random numbers tables until the required number of simulations are generated.
Step 5: Examine the results and validate the model.