Development of alternative data normalization methods for the MCRAT method in multi-criteria decision-making

This study addresses the challenge of extending the applicability of the MCRAT (Multiple Criteria Ranking by Alternative Trace) method when the standard data normalization method fails due to zero elements in the decision matrix. To achieve this, the study explores alternative normalization methods. The objectives were to identify suitable normalization methods and verify their effectiveness when combined with the MCRAT method. Three cases were analyzed: ranking nine metal cutting alternatives with one "the larger the better" and three "the smaller the better" criteria, ranking nine metal milling alternatives with one "the larger the better" and one "the smaller the better" criterion, and ranking fourteen blast hole design alternatives in the mining industry with four "the larger the better" and two "the smaller the better" criteria. Despite differences in the cases, the study discovered two additional normalization methods that, when used with MCRAT, consistently identified the best alternative. This discovery confirms that MCRAT can be applied effectively even with zero elements in the decision matrix, thus significantly extending its applicability and providing enhanced decision-making benefits. By addressing this critical limitation, the study offers a significant contribution to the field of multi-criteria decision-making by expanding the range of tools available to practitioners and researchers. The enhanced MCRAT method, equipped with new normalization capabilities, is poised to become a more versatile and powerful tool in multi-criteria decision-making, ensuring that decision-makers can make more informed and accurate choices even in challenging situations. This extension marks a notable advancement, broadening the scope and utility of the MCRAT method across different sectors and decision-making scenarios