Simulation optimization: methods and applications
Proceedings of the 29th conference on Winter simulation
Research issues in metamodeling
WSC '91 Proceedings of the 23rd conference on Winter simulation
ACM Computing Surveys (CSUR)
Clustering Algorithms
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Proceedings of the 33nd conference on Winter simulation
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Simulation with Arena (McGraw-Hill Series in Industrial Engineering and Management)
Simulation with Arena (McGraw-Hill Series in Industrial Engineering and Management)
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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A metamodel in simulation modeling, as also known as response surfaces, emulators, auxiliary models, etc. relates a simulation model's outputs to its inputs without the need for further experimentation. A metamodel is essentially a regression model and mostly known as "the model of a simulation model". A metamodel may be used for Validation and Verification, sensitivity or what-if analysis, and optimization of simulation model. In this study, we proposed a new metamodeling approach by using multiple regression integrated K-means clustering algorithm especially for simulation optimization. Our aim is to evaluate the feasibility of a new metamodeling approach in which we create multiple metamodels by clustering input-output variables of a simulation model according to their similarities. In this approach, first, we run the simulation model of a system, second, by using K-Means clustering algorithm, we create metamodels for each cluster, and third, we seek the minima (or maxima) for each metamodel. We also tested our approach by using a fictitious call center. We observed that this approach increases the accuracy of a metamodel and decreases the sum of squared errors. These observations give us some insights about usefulness of clustering in metamodeling for simulation optimization.