# Overview of Modeling Components and Processes¶

Pyomo supports an object-oriented design for the definition of optimization models. The basic steps of a simple modeling process are:

- Create model and declare components
- Instantiate the model
- Apply solver
- Interrogate solver results

In practice, these steps may be applied repeatedly with different data or with different constraints applied to the model. However, we focus on this simple modeling process to illustrate different strategies for modeling with Pyomo.

A Pyomo *model* consists of a collection of modeling *components* that
define different aspects of the model. Pyomo includes the modeling
components that are commonly supported by modern AMLs: index sets,
symbolic parameters, decision variables, objectives, and constraints.
These modeling components are defined in Pyomo through the following
Python classes:

## Set¶

set data that is used to define a model instance

## Param¶

parameter data that is used to define a model instance

## Var¶

decision variables in a model

## Objective¶

expressions that are minimized or maximized in a model

## Constraint¶

constraint expressions that impose restrictions on variable values in a model