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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 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. "[https://orbi.uliege.be/bitstream/2268/155988/1/Nguyen%20AT.pdf 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. 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.<ref>Carson, Yolanda, and Anu Maria. "[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.9192&rep=rep1&type=pdf Simulation optimization: methods and applications]." ''Proceedings of the 29th conference on Winter simulation''. IEEE Computer Society, 1997.</ref>
Specific simulation–based optimization methods can be chosen according to figure 1 based on the decision variable types.<ref>Jalali, Hamed, and Inneke Van Nieuwenhuyse. "[https://core.ac.uk/download/pdf/34623919.pdf 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:
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''Optimization [[Parametric programming|parametric]] (static)'' – The objective is to find the values of the parameters, which are “static” for all states, with the goal of maximizing or minimizing a function. 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.<ref name=":0" />
''Optimization [[Optimal control|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.<ref name=":0">Abhijit Gosavi, [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.5587&rep=rep1&type=pdf Simulation‐Based Optimization: Parametric Optimization Techniques and Reinforcement Learning], Springer, 2nd Edition (2015)</ref>
== Simulation-based optimization methods ==
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== Limitations ==
Simulation based optimization has some limitations, such as the difficulty of creating a model that imitates the dynamic behavior of a system in a way that is considered good enough for its representation. Another problem is complexity in the determining uncontrollable parameters of both real-world system and simulation. Moreover, only a statistical estimation of real values can be obtained. It is not easy to determine the objective function, since it is a result of measurements, which can be harmful for the solutions.<ref>Prasetio, Y. (2005). ''[https://elibrary.ru/item.asp?id=9387151 Simulation-based optimization for complex stochastic systems]''. University of Washington.</ref><ref>Deng, G., & Ferris, Michael. (2007). ''Simulation-based Optimization,'' ProQuest Dissertations and Theses</ref>
==References==
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