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Simulation in brief:
A mathematical model once constructed may be used to predict the consequences of taking alternative action. In particular, we could ‘experiment’ on the model by ‘trying’ alternative actions or parameters and compare their consequences. This ‘experimentation’ allows us to answer the ‘what if’ question relating the effects of assumptions on the model response. Comparing the consequences of substituting various parameters into the model is referred to as simulating the model.
Simulation is used in almost all fields, restricted only by our imagination and our ability to translate such imagination into a set of computer directives. Here are some examples:
(1) Location of ambulance
(2) Design of computer systems
(3) Shop floor management
(4) Design of optimal replenishment policy
(5) Design of a queuing system
Today, a modern game like monopoly simulates the competitive arena of real estate. Other game that people play generates experiences, understanding and gain knowledge, which is basically what we will be trying to do through simulation.
Definition of Simulation:
1) “Simulation is the process of designing a model of a real system and conducting experiments with this model for the purpose of understanding the behavior (within the limits imposed by a criterion or a set of criteria) for the operation of system.” – Shannon
2) “Simulation is the use of a system model that has the designed characteristics of reality in order to produce the essence of actual operation.” – Churchman
For OR, simulation is a problem solving technique that uses a computer-aided experimental approach to study problems that cannot be analyzed using direct and formal analytical methods. As a result simulation can be thought of as a last resort technique. It is not a technique that should be applied to all cases.
Types of Simulation:
(1) Deterministic and probabilistic simulation
(2) Time dependent and time independent simulation
(3) Visual interactive simulation
(4) Business games
(5) Corporate and financial simulations
Steps of Simulation Process:
(1) Identify the problem
(2) Identify the decision variables and decide the performance objective
(3) Construct a simulation model
(4) Testing and validating model
(5) Designing the experiment
(6) Run the simulation model
(7) Evaluate the results
Monte Carlo Method:
The ‘Monte Carlo’ simulation technique involves conducting repetitive experiments on the model of the system under study, with some known probability distribution to draw random samples (observations) using random numbers. If a system cannot be described by a standard probability distribution such as normal, Poisson, exponential, etc, an empirical probability distribution can be constructed. The Monte Carlo simulation technique consists of the following steps:
(1) Setting up a probability distribution for variables to be analyzed.
(2) Building a cumulative probability distribution for each random variable.
(3) Generating random numbers and then assigning an appropriate set of random numbers to represent value or range (interval) of values for each random variable.
(4) Conducting the simulation experiment using random sampling.
(5) Repeating Step – 4 until the required number of simulation runs has been generated.
(6) Designing and implementing a course of action and maintaining control.
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