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'''Simulation-based optimization''' integrates [[optimization (mathematics)|optimization]] techniques into [[computer simulation|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’.<ref>Nguyen, Anh-Tuan, Sigrid Reiter, and Philippe Rigo. "A review on simulation-based optimization methods applied to building performance analysis."''Applied Energy'' 113 (2014): 1043–1058.</ref>
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 experimentexperiments for each scenario. For example, there might be sotoo many possible values for input variables, or the simulation model might be sotoo 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.<ref>Carson, Yolanda, and Anu Maria. "Simulation optimization: methods and applications." ''Proceedings of the 29th conference on Winter simulation''. IEEE Computer Society, 1997.</ref>
Specific simulation basedsimulation–based optimization methods can be chosen according to figure 1 based on the decision variable types.<ref>Jalali, Hamed, and Inneke Van Nieuwenhuyse. "Simulation optimization in inventory replenishment: a classification." IIE Transactions 47.11 (2015): 1217-1235.</ref>
[[File:Slide1 1.jpg|thumb|Fig.1 Classification of simulation based optimization according to variable types]]
[[Optimization (computer science)|Optimization]] exists in two main branches of operational research:
''Optimization [[Parametric programming|parametric]] (static)'' – theThe objective is to find the values of the parameters, which are “static” for all states, with the goal of maximizemaximizing or minimizeminimizing a function. In this case, thereone is thecan 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 ofexcessive computer time, due to its complexity.<ref name=":0" />
''Optimization [[Optimal control|control]] (dynamic)'' – This is used largely in [[computer sciencesscience]] 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. ThereOne iscan use of mathematical programming, as well as dynamic programming. In this scenario, simulation can generate random samples and solve complex and large-scale problems.<ref name=":0">Abhijit Gosavi, Simulation‐Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, Springer, 2nd Edition (2015)</ref>
== Simulation-based optimization methods ==
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