In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed.

The main aim of this paper is to present a novel multi-objective gray wolf optimization (MOGWO) by utilizing the Kriging meta-model. To this end, surrogate models are used in Multi-Objective Gray Wolf Optimizer as the fitness function. The meta-model is obtained based on exact analysis and numerical simulations. Inheritable Latin Hypercube Design (ILHD) is used as the design of experiments for generation and testing the Kriging model. Then, sensitivity analysis is done to evaluate the effect of design parameter on system responses. The sensitivity analysis leads to appropriate selection of optimization design variables. Hence, the MOGWO algorithm is applied to the problem, the set of non-dominated optimal points are obtained as Pareto Front and one optimal point is selected based on the minimum distance approach. The most important purpose of the methodology is to improve the time consuming in multi-objective optimization problems. In conclusion, for the design of hydrazine catalyst bed was utilized from the proposed methodology. In case, design variables are catalyst bed pellet diameter, loading factor, thrust chamber pressure and Reaction efficiency and objective functions are increasing performance and reducing mass and pressure drop. The results of optimal catalyst bed parameters and also corresponding value of objective functions are shown the performance of methodology in the space propulsion system applications.

Climate change is caused by temperature, rainfall, and wind variation in locations that last a long time. This change can be described and predicted using a spatial model, one of which is the Clustering Spatial Autoregressive (SAR) Kriging model. Therefore, this research aims to conduct a bibliometric analysis in a spatial and Clustering SAR Kriging model on climate change. It presents a Systematic Literature Review (SLR) with the development of the Clustering SAR Kriging model, incorporating articles from the Google Scholar, ScienceDirect, Dimensions AI, and Scopus databases from 2011-2021. Furthermore, two stages of analysis have been conducted, first, bibliometric analysis was performed for mapping and thematic evolution using VOSviewer software and R-biblioshiny. This analysis generated 185 papers after conducting a duplication check and developed a network of research on evolutionary subject matters at this stage. Second, research subjects were analyzed using the Clustering SAR Kriging model. More screening criteria were followed, and 18 articles were obtained for the SLR analysis. Furthermore, the development of the Clustering SAR Kriging model was observed for the prediction and description of climate change. The results are predicted to benefit applicable businesses to predict climate phenomena in unobserved places.