Simulation-based optimization: Difference between revisions

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{{Profile UU|name=Karin Westlund|occupation=PhD student|department=Civil and Industrial Engineering|research area=Applied simulation-based multi-objective optimization for supply chains}}{{short description|Form of optimization}}

{{short description|Form of optimization}}

'''Simulation-based optimization''' (also known as simply '''simulation optimization''') integrates [[optimization (mathematics)|optimization]] techniques into [[computer simulation|simulation]] modeling and analysis. Because of the complexity of the simulation, the [[objective function]] may become difficult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that the objective function must be estimated using statistical estimation techniques (called output analysis in simulation methodology).

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> or 'simulation-based multi-objective optimization' used when more than one objective is involved.

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 suboptimala large set of input variable values. In these cases, the goal is to iterative 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 Winter Simulation Conference''. 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>