A Nonlinear Approach for Neutrosophic Linear Programming

Traditional linearl programming usually handles optimization problems involving deterministic objective functions and/or constrained functions. However, uncertainty also exists in real problems. Hence, many researchers have proposed uncertain optimization methods, such as approaches using fuzzy and stochastic logics, interval numbers, or uncertain variables. However, in practical situations, we often have to handle programming problems involving indeterminate information. The aim of this paper is to put forward a new algorithm for solving the Single-Valued Neutrosophic linear programming problem. A numerical example is reported to verify the effectiveness of the new algorithms.