  In this paper, we present a novel method for solving multiobjective linear
programming problems (MOLPP) that overcomes the need to calculate the optimal
value of each objective function. This method is a follow-up to our previous
work on sensitivity analysis, where we developed a new geometric approach. The
first step of our approach is to divide the space of linear forms into a finite
number of sets based on a fixed convex polygonal subset of $\mathbb{R}^{2}$.
This is done using an equivalence relationship, which ensures that all the
elements from a given equivalence class have the same optimal solution. We then
characterize the equivalence classes of the quotient set using a geometric
approach to sensitivity analysis. This step is crucial in identifying the ideal
solution to the MOLPP. By using this approach, we can determine whether a given
MOLPP has an ideal solution without the need to calculate the optimal value of
each objective function. This is a significant improvement over existing
methods, as it significantly reduces the computational complexity and time
required to solve MOLPP.
  To illustrate our method, we provide a numerical example that demonstrates
its effectiveness. Our method is simple, yet powerful, and can be easily
applied to a wide range of MOLPP. This paper contributes to the field of
optimization by presenting a new approach to solving MOLPP that is efficient,
effective, and easy to implement.
