Standard textbooks on operational research or optimization techniques often distinguish between model simulation and model optimization. The same distinction is often made by practical modelers. We shall show here, however, that advanced model analysis might combine both these functions, together with multi-objective model optimization, or special functions of inverse model simulation, or softly constrained simulation. In this sense, we can speak of multi-objective model analysis or even multi-objective modeling. Such an approach uses various optimization tools and other algorithms, but not as a goal, only as a tool of multi-objective analysis. This approach differs essentially from the classic one, where optimization is viewed as a goal, a tool of ultimate, optimal choice. At the end of this chapter we comment also on the role of final choice in modeling for decision support; however, here we concentrate more on supporting learning than on supporting choice.
Unable to display preview. Download preview PDF.