What Is Optimization?

Optimization is the process of trying to find the “best” solution to a problem. For example, a company might have three manufacturing plants, each manufacturing different quantities of different goods. Given the cost for each plant to produce each good, the costs for each plant to ship to each store, and the limitations of each plant, the goal is to find the optimal way to adequately meet the demand of local retail stores while minimizing the transportation costs.

This is the type of problem that optimization is designed to answer, but it is certainly not the only type. Optimization models of various types appear in the fields of operations management, finance, marketing, economics, engineering, and others. They all share a common goal - to select values of inputs to maximize (or minimize) an objective value, possibly subject to constraints.

Traditionally, most optimization models have been deterministic, meaning that they don’t include any explicit uncertainty. For example, if a demand forecast is part of the model, it is entered as a fixed number, with no uncertainty. Evolver is designed to optimize only deterministic models. If your model include explicit uncertainty, in the form of probability distributions for some of the variables, you can use Palisade’s companion product RISKOptimizer, which is integrated into @RISK.

Evolver and RISKOptimizer are quite similar in many ways, but they differ in one fundamental way - when Evolver needs to check how “good” a potential solution is, all it has to do is evaluate spreadsheet formulas, such as the formula for profit, once. In contrast, RISKOptimizer has to run a simulation to discover the range of possible profits for that particular solution. So RISKOptimizer is able to solve a larger class of problems, those with uncertainty, but it tends to be much slower than Evolver.