Parameter optimization is used to identify optimal settings for the inputs that you can control. Companion searches a range of values for each input to find settings that meet the defined objective and lead to better performance of the system. After a simulation analysis, you can perform a parameter optimization or a sensitivity analysis, However, for engineering applications, you typically perform a parameter optimization before a sensitivity analysis because changing the system settings is often easier than changing the variability of the inputs. For example, adjusting the temperature setting is easier than reducing the variability of the temperature.
Consider the construction project scenario introduced in Monte Carlo simulation. The initial simulation answers the business question "What percent of projects will take more than 30 business days?". To explore ways to reduce the percent of projects that extend past the upper limit of 30 business days, you can use parameter optimization.
When you perform a parameter optimization, Companion searches for alternative input settings that optimize an output based on the objective and the search range you define. For the construction project scenario, you want to find optimal settings for the inputs that reduce the percent of projects that extend past 30 days (% out of spec).
Consider using search ranges that are as wide as possible to broaden the search area and increase your chances of meeting your objective. Do not exceed levels that are unfeasible or unsafe for your system. You can repeat the parameter optimization and see how changing the search range affects the estimates of performance.
Companion displays the results of the parameter optimization, assumptions, and guidance for next steps.
Each time you repeat the simulation, the results will vary because the simulation is based on randomly selected values for the inputs.
After you analyze the results, you may want to return to the model and change inputs or outputs, and then rerun it. This allows you to test a number of "what if" scenarios allowing you to gain insight into the behavior of your system and make better decisions.