Resumo:
The manufacturing system modeling through simulation is used since the early 60’s
and became one of the most popular and powerful tools for analyzing complex manufacturing
systems. Through system modeling is possible perform its optimization. However, the
integration between optimization and simulation did not happen fast. In fact, until the end of
last millennium, optimization and simulation were kept well separated but this situation has
changed and, nowadays, optimization software is a component of almost every simulation
package.
Optimization via simulation demands a considerable computational effort since to
locate the optimum solution it is necessary to verify several parameter value settings. One
way to accelerate the optimization is reducing the search space by selecting the variables
which comprises it, once not all variables have the same importance with respect of their
effect over the model output.
This research has studied the use of fractional factorial design statistic techniques to
identify the more important variables from two discrete-event simulation models aiming to
reduce the optimization’s space search in order to accelerate that phase. This is an applied
quantitative experimental research, with explanatory objective. The tool to perform the
experiments is the discrete-event simulation.
The research methodology was to optimize each model by two distinct procedures.
The first procedure performs a variable sensitivity analysis of the model using fractional
factorial designs. After identifying the more important variables, the model’s optimization is
performed using this reduced search space. The second procedure performs a straightforward
model’s optimization. No study was done in this approach to determine if all model’s
variables impact the same effect to the output. Finally, for each model, the amount of runs of
each procedure was compared.
The result of the first application appointed a 59% reduction for the amount of runs
between the planned optimization and the straightforward one. The second application did not
present such reduction. The main reason for this bad result in the last application is the way
the system was modeled, showing the importance of planning the system’s model correctly.
