Genetic algorithm for computing the optimal values of interval linear programming problems

In many real life problems, system parameters or model coefficients may be bounded between lower and upper bounds due to a variety of uncertainties. Over the past 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. Computing the best value is easy, but obtaining the worst value is very complicated or even impossible. In this research, we obtain the optimal range of interval problem via genetic algorithm and provide one illustrative example to verify and compare the obtained results with the exact optimal range.