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This paper compares two metamodel-based methodologies for multi-criteria simulation optimization (SO), using the injection molding of a disposable camera front housing as a case study. The first methodology uses linear regression metamodels and data envelopment analysis (DEA) to iteratively identify potential Pareto solutions, while the second one uses a Gaussian process metamodel and calculates an expected improvement to determine the new input runs sequentially. A one-to-one comparison of the approaches is presented using two optimization examples. The first example involves two process outputs and the second involves three. The approaches are evaluated using the same number of simulations, and are compared in terms of the quality of the obtained Pareto front, based on the hypervolume indicator. Advantages and disadvantages of both methods are discussed.

A comparison of two metamodel-based methodologies for multiple criteria simulation optimization using an injection molding case study