Simulation-based optimization: Difference between revisions

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Revision as of 00:30, 25 May 2016

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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 sometimesthe thereinterest areis interestsometimes 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 experiment for each scenario. For example, there might be so many possible values for input variables, or simulation model might be so complicated and expensive to run for suboptimal input variable values. In these cases, the goal is to find optimal values for 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 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>

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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 this kind of method is [[Tabu search|tabu search]] or genetic[[Genetic algorithm]].<ref name=":0" />

=== Stochastic approximation ===

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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[[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). Neuro-dynamic programming approach to retailer inventory management. ''Proceedings of the IEEE Conference on Decision and Control,'' ''4'', 4052-4057.</ref>

== Limitations ==

Simulation basebased optimization has some limitations, such as the difficulty of createcreating a model that imitates the dynamic behavior of thea system in a way that is considered good enough for its representation. Other problem is howcomplexity complex it isin the determination ofdetermining uncontrollable parameters of theboth real-world system and of the simulation. Moreover, only a statistical estimation of the real values can be obtained. It is not easy to determine the objective function, since it is a result of measurements, whatwhich can be harmful for the solutions.<ref>Prasetio, Y. (2005). ''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>

== Application ==