Calculating The Range Of Optimal Values Of The Interval Linear Programming Problems: Comparing Genetic Algorithm With Monte Carlo Simulation

In many real world Issues, system parameters or model coefficients may be limited between lower and upper Limitations due to various types of uncertainties. In studies conducted in previous decades, intensive research efforts have focused on interval problems by two sub-models to tackle such uncertainties. In most of methods, interval problems by two sub-models (best and worst models) with deterministic parameters are formulated. Calculating the best value is easy, but obtaining the worst value is very complicated or even impossible. In this article, we obtain the optimal range of interval problem via genetic algorithm and present one illustrative example to verify and compare the obtained results of it with the obtained results through Monte Carlo simulation and the exact optimal values.