A parametric approach to solve neutrosophic linear programming models

Abstract

When solving a linear programming model, the coefficients should be fixed at specific values in advance. In practice, however, data overwhelmingly lack precision and this affects the model’s optimal solution. Among other theories including fuzzy set and intuitionistic fuzzy set theories, the neutrosophic set theory is considered a generalization of the two theories mentioned and is shown to be very powerful in assimilating inaccurate, vague, and maladjusted data. In this study, we deal with neutrosophic linear programming models where all coefficients are represented by triangular neutrosophic numbers. Maximization, minimization, and all types of constraints are considered. A novel parametric-based approach is introduced to solve this type of model and a few numerical examples are provided. Results show that the presented approach yields more realistic solutions. Finally, we conclude that the proposed approach is efficient, flexible, and capable of solving neutrosophic linear programming models.