Simulation-based optimization

Simulation-based optimization integrates optimization techniques into simulation analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate.

Once a system is mathematically modeled, computer-based simulations provide the 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. Such methods are known as ‘numerical optimization’ or ‘simulation-based optimization’.

In simulation experiment, the goal is to evaluate the effect of different values of input variables on a system, which is called running simulation experiments. 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 experiment for each scenario. For example, there might be so many possible values for input variables, or simulation model might be so complicated and expensive to run for suboptimal input variable values. In these cases, the goal is to find optimal values for 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 operational research:

Optimization parametric (static) – the objective is to find the values of the parameters, which are “static” for all states, with the goal of maximize or minimize a function. In this case, there is the use of mathematical programming, such as linear programing. In this scenario, simulation helps when the parameters contain noise or the evaluation of the problem would demand excess of computer time, due to its complexity.

Optimization control (dynamic) – used largely in computer sciences and electrical engineering, what results in many papers and projects in these fields. The optimal control is per state and the results change in each of them. There is use of mathematical programming, as well as dynamic programming. In this scenario, simulation can generate random samples and solve complex and large-scale problems.

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