How can you select and validate assumptions, parameters, and objectives for optimization models?
Optimization models are mathematical tools that help you find the best solutions to complex problems involving multiple decisions, constraints, and objectives. They are widely used in industrial engineering to improve the efficiency, productivity, and quality of various systems and processes. However, building and using optimization models requires careful selection and validation of the assumptions, parameters, and objectives that define the problem and the solution. In this article, you will learn how to do that in four steps.