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=== Response surface methodo<nowiki/>logy (RSM)===
In [[response surface methodology]], the objective is to find the relationship between the input variables and the response variables. The process starts from trying to fit a linear regression model. If the P-value turns out to be low, then a higher degree polynomial regression, which is usually quadratic, will be implemented. The process of finding a good relationship between input and response variables will be done for each simulation test. In simulation optimization, response surface method can be used to find the best input variables that produce desired outcomes in terms of response variables.<ref>Rahimi Mazrae Shahi, M., Fallah Mehdipour, E. and Amiri, M. (2016), [https://onlinelibrary.wiley.com/doi/abs/10.1111/itor.12150 Optimization using simulation and response surface methodology with an application on subway train scheduling]. Intl. Trans. in Op. Res., 23: 797–811. doi:10.1111/itor.12150</ref>
=== Heuristic methods ===
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==== Neuro-dynamic programming ====
Neuro-dynamic programming is the same as dynamic programming except that the former has the concept of approximation architectures. It combines [[artificial intelligence]], simulation-base algorithms, and functional approach techniques. “Neuro” in this term origins from artificial intelligence community. It means learning how to make improved decisions for the future via built-in mechanism based on the current behavior. The most important part of neuro-dynamic programming is to build a trained neuro network for the optimal problem.<ref>Van Roy, B., Bertsekas, D., Lee, Y., & Tsitsiklis, J. (1997). [https://web.stanford.edu/~bvr/pubs/retail.pdf Neuro-dynamic programming approach to retailer inventory management]. ''Proceedings of the IEEE Conference on Decision and Control,'' ''4'', 4052-4057.</ref>
== Limitations ==
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