A REVIEW ON RECENT LITERATURES ON RESPONSE SURFACE METHODOLOGY AND ITS APPLICATIONS

Introduction

Response Surface Methodology (RSM) remains a powerful and widely adopted statistical and mathematical tool for process optimization and modeling. Recent literature from 2020 onwards highlights its enduring relevance while also showcasing significant advancements, particularly its integration with modern computational techniques and its application in new and complex fields.

Response Surface Methodology (RSM) has solidified its position as a powerful collection of statistical and mathematical techniques for designing experiments, developing empirical models, and optimizing processes where a response of interest is influenced by several independent variables. Since 2020, research has not only expanded its traditional applications but also integrated it with emerging technologies like artificial intelligence and machine learning to overcome its limitations and enhance predictive capabilities. This review synthesizes key findings from a wide range of recent literature, highlighting both the continued utility of classic RSM designs and the innovative directions of modern research.

The foundation of RSM remains the approximation of an unknown functional relationship between a response (y) and a set of independent variables (X1, X2,...,Xk) using a low-degree polynomial model, typically a second-order model:

Y = β₀+ ∑β_iX_i1+ ∑β_iiX_i2+ ∑_i_<_jβ_ijX_iX_j+ϵ

This approach allows for the efficient exploration of a design space with a minimal number of experimental runs.

This review synthesizes key findings from a wide range of recent literature, highlighting both the continued utility of classic RSM designs and the innovative directions of modern research.

This review synthesizes findings from a broad range of scientific journals, focusing on key trends in RSM's methodological developments and its diverse applications across various disciplines.

A review of Response Surface Methodology (RSM) in Recent Literature

1. Response Surface Methodology Using Observational Data: A Systematic Literature Review

This systematic review surveys methods that adapt classical RSM (designed for interventional experiments) to observational datasets. It catalogs approaches (statistical corrections, causal assumptions, modeling tweaks), discusses limitations—especially confounding and lack of randomization—and proposes frameworks for when and how observational RSM can be defensibly applied. The paper emphasizes the need for careful assumptions, diagnostic checks, and the use of supplementary causal inference tools. Hadiyat, Sopha & Wibowo (2022).

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