Long gone are the days of trial-and-error. Say hello to finding the best decision quickly and efficiently with optimization using Simul8!  

Our optimization integration, OptQuest, has undergone a makeover and today we will reveal the top 3 tips on using optimization and simulation together.  

#1: Scope the problem as tightly as you can 

What are you asking the optimization to answer? Being very clear about the problem you are trying to solve involves not just defining your objective function but doing so in a way that makes it easier to find the optimal solution.  

For example, you may be interested in increasing throughput, and you have a series of decision variables to help you get there. Once you plug that into OptQuest, however, you may find that while your throughput has increased, so has the number of defective products. Not ideal, right? 

Adding a further objective to minimize faulty products will allow you to have a well-defined problem that makes it easier for OptQuest to focus its efforts on the areas that matter most. 

Lastly, make sure you reflect on and include any constraints that the solution will have to abide by, and avoid adding variables that may not have a direct impact on your chosen objectives. 

#2: Scale the number of trials in line with decision variables 

Optimization is a great way to get the best answer, especially when there are many decision variables at play. It’s worth noting that each variable you include adds a layer of complexity to the response surface, meaning it can get more difficult and time-consuming to find that sweet spot you’re after.  

That’s why we recommend increasing your optimization run settings as you add more decision variables. More iterations = more chances of finding the best solution!

#3: Tighten your variables’ bounds  

So, we’ve seen how more variables require more trials to be carried out. To further smooth out the response surface, consider tightening the bounds of your decision variables. 

Think about whether a variable needs to be in the range of 0 to 100. If said variable took a value of 0 in a potential optimal solution, would it realistically be implemented?  

Using sensible values for the upper and lower bounds of your variables can once again simplify the search for the best answer.  

Check out our step-by-step OptQuest tutorial to explore how optimization be implemented within Simul8.  

If you are interested in adding OptQuest to your Simul8 subscription, get in touch with our simulation advisers today for more information.  

Happy Simul8ing!