Description: Infeasibility in the context of model optimization refers to a situation where there is no solution that satisfies all the constraints imposed by an optimization problem. This can occur in various scenarios, such as linear programming, where the constraints may be incompatible with each other, resulting in an empty set of solutions. Infeasibility is a crucial concept because it indicates that the formulated model needs to be reviewed or adjusted to find a viable solution. The main characteristics of infeasibility include the identification of contradictory constraints, the impossibility of satisfying all conditions of the problem, and the need to reformulate the model to achieve a balance between constraints and objectives. The relevance of understanding infeasibility lies in its impact on decision-making; recognizing that a model is infeasible can lead analysts to reconsider initial assumptions and explore alternatives that allow for an optimal outcome. In summary, infeasibility is a state that must be addressed in the optimization process, as its identification is fundamental to the success of any model analysis.
