In this method, the input of each variable is varied with other parameters remaining constant and the effect on the design objective is observed.
However, the interest is sometimes in finding the optimal value for input variables in terms of the system outcomes.
However, this approach is not always practical due to several possible situations and it just makes it intractable to run experiments for each scenario.
[2] Specific simulation–based optimization methods can be chosen according to Figure 1 based on the decision variable types.
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.
The process of finding a good relationship between input and response variables will be done for each simulation test.
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.