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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
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>
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=== Heuristic methods ===
[[Heuristic (computer science)|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
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. <ref>{{Cite journal|last=Yousefi|first=Milad|last2=Yousefi|first2=Moslem|last3=Ferreira|first3=Ricardo Poley Martins|last4=Kim|first4=Joong Hoon|last5=Fogliatto|first5=Flavio S.|title=Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments|journal=Artificial Intelligence in Medicine|volume=84|pages=23–33|doi=10.1016/j.artmed.2017.10.002|pmid=29054572|year=2018}}</ref>
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