Simulation Optimization

Optimization can be tricky. Many real-world problems are too complex for a mathematical formula to find the solution. Modern systems are nonlinear, with deep inter-dependencies between resources and variability to consider at every turn. This usually means that simulation is the best (and sometimes only) way to understand and solve practical problems. With an accurate simulation model of your system, you are free to test out a variety of different scenarios to search for the configuration that makes the most sense for your organization.

Or… you can combine simulation with the best optimization techniques and make the best possible decision.

Think of simulation optimization like a set of driving directions from your favorite GPS-enabled map application. It considers all possible roads, highways, and turnpikes to show you the best ways to reach your destination. But what does “best” mean? One route might have a shorter distance to travel at the cost of a few extra minutes on the road. Another might usually be the quickest, but not during rush hour traffic. You might end up selecting a route not because it’s the fastest, but because it passes by a store that you’d like to visit. Either way, the map application has searched through an all routing possibilities to provide you the best options to choose from.

Simulation optimization will do the same for your strategic or operational decision making. Start with a simulation model of your real-world system, with all its complex behaviors and variability. Then use an optimization engine to search through an infinite number of possibilities to find the best configurations to meet your goals. Almost like an intelligent, GPS-enabled roadmap to organizational success.