Simulation models can be classified into the following 4 categories:

1. Deterministic models: In these models, input and output variables are not permitted to be random variables, and models are described by exact functional relationship.
2. Stochastic models: In these models, at least one of the variables or functional relationship is given by probability functions.
3. Static models: These models do not take variable time into consideration.
4. Dynamic models: These models deal with time varying interaction.

Advantages of simulation models:

• Simulation models are comparatively flexible, and can be modified to adjust according to the variation in the environments of real life situations.
• Simulation is easier to use than mathematical models and is hence considered superior to mathematical analysis.
• Simulation techniques have the advantage of being relatively free from complicated mathematics and hence, can be easily understood by the operating staff and also by non-technical managers.
• Simulation offers up a solution by performing virtual experimentation with a model of the system without interfering with the real system. It thus bypasses complex mathematical analysis.
• Simulation compresses the performance of a system over several years, and hence performs large calculations in a few minutes of computer running time.
• By using simulation, management can foresee the difficulties and bottlenecks that may arise due to addition of new machines or equipment, or by modifying a process. It eliminates the need for costly trial and error methods of trying out a new concept on real processes and equipment.
• It is better to train people on a simulated model, rather than putting them to work straightaway on the real system. Simulation develops the trainee making him experienced and an expert, due to which the trainee now has sufficient confidence in handling the real system.

Disadvantages of simulation models:

• Optimum results cannot be produced by simulation. Since the models only deal with uncertainties, results of simulation are merely reliable approximations involving statistical errors.
• In many situations, it isn’t possible to quantify all the variables which play a role in the system.
• In large, complex problems involving many variables and their inter-relationships, the capacity of the computer may not be enough to process the entire system.
• Since computers are involved in simulation, it makes simulation a comparatively costlier technique to use.
• Simulation is sometimes applied to simple problems, due to over reliance on simulation, when in fact the problems could be solved in an easier manner by some other technique like mathematical analysis.

Solution derived from analytical models:

• These solutions are precise, i.e. accurate, and reflect the true state of the system.
• Moreover, such derived solutions are most often the optimal solution, depicting the best state of the system.
• However, these solutions involve complex calculations which require a fair amount of time.
• Also, it is not easy to derive a solution for systems other than small scale systems.

Solutions derived from simulation models:

• These solutions are not accurate, and certainly not optimal.
• This is because there exists a lot of uncertainties that lead to various statistical errors.
• However, these solutions are quick to generate, with no complex calculations involved whatsoever.
• Also, though not accurate, they still represent a fair overview of the behavior of the system.