Illustrative Example: Production Line with 10 Workstations

 

The capability of dealing with tens of variables simultaneously opens important analysis possibilities ranging from statistical characterization to optimization. This section illustrates how a 10-variable simulation-optimization problem can be addressed aided by an experimental design with such capability.

 

      Consider a production line with 10 workstations simulated with the software package SIMIO. The simulation is run for 8 hours per day with 10 replicates.  The simulation parameters of interest were the mean process time on each of the workstations (WSi). The process time in each workstation was assumed to follow a normal distribution with a mean that varied in three levels and a constant standard deviation of 0.25 minutes. It is further assumed that the nominal process time can be chosen by a particular user. The response of interest was the system time defined as the  period of time elapsed since a raw part to be processed enters the system until it exits as a finished product.

 

      A simulation optimization method based on design of experiments and metamodeling techniques was used (Villarreal-Marroquín, 2013). The method starts with an initial experimental design, which for 10 variables has 71 experimental runs. The figure below shows the ranges of values to be explored with the objective to minimize the system time per unit.