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Learn more about HENSOLDT on our website dedicated to our annual report ! Read more. Be a pioneer! We are looking for motivated and highly qualified individuals who want to become pioneers.

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Ranking and selection methods are designed for problems where the alternatives are fixed and known, and simulation is used to estimate the system performance.

In the simulation optimization setting, applicable methods include indifference zone approaches, optimal computing budget allocation, and knowledge gradient algorithms.

In response surface methodology , the objective is to find the relationship between the input variables and the response variables. The process starts from trying to fit a linear regression model.

If the P-value turns out to be low, then a higher degree polynomial regression, which is usually quadratic, will be implemented.

The process of finding a good relationship between input and response variables will be done for each simulation test.

In simulation optimization, response surface method can be used to find the best input variables that produce desired outcomes in terms of response variables.

Heuristic methods change accuracy by speed. Their goal is to find a good solution faster than the traditional methods, when they are too slow or fail in solving the problem.

Usually they find local optimal instead of the optimal value; however, the values are considered close enough of the final solution.

Examples of these kinds of methods include tabu search and genetic algorithms. Metamodels enable researchers to obtain reliable approximate model outputs without running expensive and time-consuming computer simulations.

Therefore, the process of model optimization can take less computation time and cost. Stochastic approximation is used when the function cannot be computed directly, only estimated via noisy observations.

In this scenarios, this method or family of methods looks for the extrema of these function. The objective function would be: [9].

Derivative-free optimization is a subject of mathematical optimization. This method is applied to a certain optimization problem when its derivatives are unavailable or unreliable.

Derivative-free methods establish a model based on sample function values or directly draw a sample set of function values without exploiting a detailed model.

Since it needs no derivatives, it cannot be compared to derivative-based methods. Some methods cannot handle optimization problems with more than a few variables; the results are usually not so accurate.

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.

See, for example, the following [5] [11]. Derivative-free optimization methods are relatively simple and easy, but, like most optimization methods, some care is required in practical implementation e.

Dynamic programming deals with situations where decisions are made in stages. The key to this kind of problems is to trade off the present and future costs.

Neuro-dynamic programming is the same as dynamic programming except that the former has the concept of approximation architectures.

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