SIMULATION LANGUAGE AND SIMULATORS

SIMULATION LANGUAGE AND SIMULATORS

1. Beginning

   • Initially, the simulation programs were developed using some general purpose language, such as ASSEMBLER, FORTRAN, ALGOL or PL / I. From the 1960s, the specific languages for simulation such as GPSS, GASP, SIMSCRIPT, SLAM appear.

2. Nowadays

   • In practice, it is important to use the application that best suits the type of system to simulate, since the time of development of the simulation model will depend on the selection of the language or simulator. The options range from spreadsheets, general type languages (such as Visual Basic, C ++ or Fortan), specific simulation languages (such as GPSS, SLAM, SIMAN, SIMSCRIPT, GAS and SSED), to simulators specifically developed for different purposes ( as SIMPROCESS, ProModel, Witness, Taylor II and Crystal Ball).

3. Applications

   • The simulation languages automatically provide the necessary features for the programming of a simulation model, which results in a significant reduction in the effort required to program the model. They provide a natural framework for the use of simulation models. The basic building blocks of language are much more related to the purposes of simulation than those of a general language. Simulation models are much more easily modified. Many of them provide a dynamic allocation of memory during execution. They facilitate a better detection of errors.
     • Advantage
       • • Through a simulation study, the effect of internal and external system changes can be studied by making alterations in the system model and observing the effects of those alterations on the behavior of the system. • A detailed observation of the system being simulated can lead to a better understanding of the system and therefore to suggest strategies that improve the operation and efficiency of the system. • The simulation technique can be used as a pedagogical tool to teach students basic skills in statistical analysis, theoretical analysis, etc.

4. Practical case

   • A practical case of a simulation we can say in this part, the simulation of the Monte Carlo Method. ALGORITHMS The Monte Carlo Crude or Pure Simulation algorithm is based on the generation of random numbers by the Inverse Transformation method, which is based on the accumulated frequency distributions: