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However, this approach is not always practical due to several possible situations and it just makes it intractable to run experiments for each scenario.
For example, there might be too many possible values for input variables, or the simulation model might be too complicated and expensive to run for suboptimal input variable values.
In these cases, the goal is to find optimal values for the input variables rather than trying all possible values. This process is called simulation optimization.
Specific simulation—based optimization methods can be chosen according to Figure 1 based on the decision variable types.
Optimization exists in two main branches of operations research:. In this case, one can use mathematical programming , such as linear programming.
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.
Optimization control dynamic — This is used largely in computer science and electrical engineering. The optimal control is per state and the results change in each of them.
One can use mathematical programming, as well as dynamic programming. In this scenario, simulation can generate random samples and solve complex and large-scale problems.
Some important approaches in simulation optimization are discussed below. 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. In South Africa we are manufacturing in Pretoria as well as in Irene.
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