Solving geometric programming problems with triangular and trapezoidal uncertainty distributions

The geometric programming problem is an important optimization technique that often used to solve different nonlinear problems and engineering problems. models are commonly generally based on deterministic accurate parameters. However, it observed in real-world problems, the parameters frequently inaccurate ambiguous. In this paper, we consider chance-constrained with uncertain coefficients techniques uncertain-based framework. We show associated can be converted into a crisp by using triangular trapezoidal uncertainty distributions for variables. main aim of paper provide solution procedures under distributions. To how well algorithms work, two numerical examples application inventory model given.