Simulation-based optimization: Difference between revisions

Revision as of 02:30, 21 October 2017

Revision as of 02:31, 21 October 2017

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[[Optimization (computer science)|Optimization]] exists in two main branches of operational research:

''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 programingprogramming]]. 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, Simulation‐Based Optimization: Parametric Optimization Techniques and Reinforcement Learning, Springer, 2nd Edition (2015)</ref>