Why Simulation

Regarding production systems analysis, discrete-event simulation (DES) is not only a popular tool for the evaluations of complex real-world systems but probably the only feasible choice, especially when the processing times and downtimes follow non-exponential or non-normal distributions. Stochastic simulation is the only available choice for researchers and practitioners in the industry alike if more complex flows and other types of variability (e.g., setups) are included in the study of unbalanced production lines. Some academician has claimed that if quantitative performance evaluation is carried out at all in the industry, then in almost any case simulation is the only tool used.

Compared to computer simulation, there are several weaknesses of analytical/mathematical modeling methods: (1) analytical evaluation is impractical when it encounters stochastic elements, such as many random and non-linear operations that exist in virtually any manufacturing system; (2) due to randomness in a dynamic system which changes with time, the mathematical modeling of a complex dynamic system requires many simplifications which may cause the models to become invalid; (3) analytical methods are not sufficient for optimization because mathematical models can only be built with simplifying assumptions that may affect the accuracy of the performance measures.