Comparative Sensitivity Analysis in Composite Material Selection: Evaluating OAT and COMSAM Methods in Multi-criteria Decision-making

Jakub Więckowski; Wojciech Sałabun

doi:10.31181/smeor21202524

Authors

Jakub Więckowski National Institute of Telecommunications, Szachowa 1, Warsaw, 04-894, Poland Author https://orcid.org/0000-0002-9324-3241
Wojciech Sałabun National Institute of Telecommunications, Szachowa 1, Warsaw, 04-894, Poland Author https://orcid.org/0000-0001-7076-2519

DOI:

https://doi.org/10.31181/smeor21202524

Keywords:

Sensitivity analysis, COMSAM, Decision-making, Results robustness, OAT, COMET

Abstract

The decision-making process is critical across various fields, influencing essential aspects like performance, safety, and sustainability. In engineering and materials science, selecting suitable materials is a complex process involving multiple interdependent criteria, which impact the reliability and lifecycle of a design. Traditional sensitivity analysis methods, such as the one-at-a-time (OAT) approach, assess the effects of individual parameter changes but often fail to capture the cumulative impact of simultaneous modifications. It can limit the effectiveness of decision tools in real-world scenarios where multiple factors vary concurrently. This study utilizes the Comprehensive Sensitivity Analysis Method (COMSAM) to address limitations in traditional sensitivity analysis by modeling simultaneous parameter adjustments across multiple criteria. Specifically, the study aims to validate COMSAM’s effectiveness in the practical problem of composite material selection, comparing it with the traditional OAT approach within a multi-criteria decision-making (MCDM) framework. By integrating both sensitivity analysis techniques with the Characteristic Objects Method (COMET), this research ensures robust evaluation and mitigates vulnerabilities like the rank reversal paradox. The findings demonstrate that COMSAM provides enhanced insights into the impact of parameter interdependencies, offering a more resilient foundation for decision-making in high-stakes environments where material properties and performance are paramount.

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