Improving The Obtained Results Of Monte Carlo Simulation For Interval Linear Programming Problem By Using Particle Swarm Optimization

In this article, we assume the interval linear programming problem with parity constraints. Since any interval linear programming problems have an infinite number of characteristic models. We are just looking for the set of all optimal amounts (best and worst amount) of the objective function (ObF). gaining the best amount of the ObF is simple, but gaining the worst amount of the ObF is NP-hard. In this research, firstly, we earn the range of optimal amounts of the ObF and then we obtain it, via particle swarm optimization method. Finally, we provide one numerical example to verify and evaluate the obtained results of it with the obtained results through Monte Carlo simulation and the exact optimal amounts.