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Usually, the underlying simulation model is stochastic, so that that the objective function must be estimated using statistical estimation techniques called output analysis in simulation methodology.
Once a system is mathematically modeled, computer-based simulations provide information about its behavior. Parametric simulation methods can be used to improve the performance of a system.
In this method, the input of each variable is varied with other parameters remaining constant and the effect on the design objective is observed. This is a time-consuming method and improves the performance partially.
To obtain the optimal solution with minimum computation and time, the problem is solved iteratively where in each iteration the solution moves closer to the optimum solution.
In simulation experiment, the goal is to evaluate the effect of different values of input variables on a system. However, the interest is sometimes in finding the optimal value for input variables in terms of the system outcomes.
One way could be running simulation experiments for all possible input variables. 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.
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