Simulation-based optimization

In this method, the input of each variable is varied with other parameters remaining constant and the effect on the design objective is observed.In this scenario, simulation helps when the parameters contain noise or the evaluation of the problem would demand excessive computer time, due to its complexity.In this scenario, simulation can generate random samples and solve complex and large-scale problems.[5] [6] Ranking and selection methods are designed for problems where the alternatives are fixed and known, and simulation is used to estimate the system performance.Their goal is to find a good solution faster than the traditional methods, when they are too slow or fail in solving the problem.[4] Metamodels enable researchers to obtain reliable approximate model outputs without running expensive and time-consuming computer simulations.However, there are numerous practical cases where derivative-free methods have been successful in non-trivial simulation optimization problems that include randomness manifesting as "noise" in the objective function.It means learning how to make improved decisions for the future via built-in mechanism based on the current behavior.The most important part of neuro-dynamic programming is to build a trained neuro network for the optimal problem.[13] Simulation-based optimization has some limitations, such as the difficulty of creating a model that imitates the dynamic behavior of a system in a way that is considered good enough for its representation.