Metamodeling by using multiple regression integrated K-means clustering algorithm

Abstract

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 need for further experimentation. A metamodel is essentially a regression model and mostly known as the model of a simulation 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 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 simulation model of a system, second, by using K-Means clustering algorithm, we create metamodels for each cluster, and third, we seek minima (or maxima) for each metamodel. We also tested our approach by using a fictitious call center. We observed that this approach increases accuracy of a metamodel and decreases sum of squared errors. These observations give us some insights about usefulness of clustering in metamodeling for simulation optimization.