AN APPLICATION OF BOUNDED AUTOCATALYTIC SET WITH SHANNON ENTROPY FOR DESKTOP COMPUTER SELECTION

Abstract

This article proposes a multi-criteria decision-making (MCDM) approach by integrating the Bounded Autocatalytic Set (BACS) method with Shannon entropy for desktop computer selection. Existing Graph Theory and Matrix Approach (GTMA)-based decision models often neglect the weighting of criteria, which may lead to less reliable ranking outcomes. To address this limitation, the proposed method incorporates Shannon entropy to objectively determine attribute weights before applying the BACS framework for ranking alternatives. The integration provides methodological advantages by combining a graphical representation of attribute interrelationships with entropy-based weighting, resulting in a more systematic and informative decision-making process. A numerical example involving five desktop computer models (M1–M5), evaluated across five attributes, is presented to demonstrate the applicability of the method. The results show that Model M3 achieves the highest permanent index value of 8.0396, indicating that it is the most preferred of the models being evaluated. The ranking results are further validated through comparison with the Graph Theory and Matrix Approach (GTMA) and a hybrid Analytic Hierarchy Process (AHP) – Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Spearman correlation coefficients of 0.9 indicate a strong agreement between the proposed method and existing approaches, thereby confirming the reliability of the ranking outcomes. The findings demonstrate that the integration of BACS and Shannon entropy provides an effective and robust framework for solving MCDM problems.