The profit or yield of complex industrial processes can be maximized (or costs minimized) using software optimization schemes. The parameter of interest can be either maximized, minimized, or forced to a target value. This is accomplished by computing the optimum mix or combination of other (independent) process variables.
Typical applications include:
- Cost minimization of a product comprised of a mix of other products (each of which may also have multiple constituents)
- Determining the lowest-cost method for producing an alloy using a blend of other alloys or metal feedstocks in inventory
- Maximizing plant profit by selecting the optimum production process, based on available raw materials and associated costs
Optimization is a subset of Operations Research technology. It is particularly well suited for problems having more variables than equations, for which there is no unique solution.
The problem is usually described with an equation on the output variable (the objective function equation) and and several other equations or constraints on the independent variables. The equations and constraints may be either linear or nonlinear. A number of solutions meeting the constraints are often possible, but the optimization scheme selects the solution that provides the desired outcome (a maximum or minimum of the objective function).