Enhancing Experimental Efficiency in Uncertain Data: A Comparative Analysis of Neutrosophic and Classical Latin Square Designs
Data uncertainty; Experimental design efficiency; Neutrosophic latin square design; Neutrosophic logic; Neutrosophic statistics
This research investigates the relative efficiency between Neutrosophic Latin Square Design (NLSD) and Classical Latin Square Design (CLSD), with a particular focus on their use in situations where data is uncertain and ambiguous. Although CLSD is a classic experiment designed for systematic error control, its utility is limited in fields like agriculture and behavioral sciences due to its performance bottleneck regarding data imprecision. The NLSD can relatively easily be extended to incorporate neutrosophic logic to address these challenges, making it a more powerful tool for modeling uncertainty. In this paper, a systematic efficiency evaluation of NLSD against CLSD is performed for inconsistent data. It is found that the NLSD enables significant improvements in experimental efficiency while providing clearer inferences regarding treatment effects and supporting more reliable conclusions. Despite these limitations, these benefits establish NLSD as a promising candidate for overcoming environmental uncertainties, and these observations hold significant potential to further the advancement of experimental designs. The results demonstrate that NLSD conveys a 55 % chance to enhance efficiency relative to LSD, which is especially important in processes that must attain maximum resource utilization and high experimental efficiency. 2025, Ayandegan Institute of Higher Education. All rights reserved.
Kumari, Srishti; Shahabudheen, Azarudheen
International Journal of Research in Industrial Engineering;Volume;14;Issue;2;pp.318-333
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.22105/riej.2025.503412.1532 https://www.scopus.com/pages/publications/105007765181?origin=resultslist
Kumari S., Department of Statisitcs and Data Science, CHRIST (Deemed to be University), Bangalore, India; Shahabudheen A., Department of Statisitcs and Data Science, CHRIST (Deemed to be University), Bangalore, India