Multi-criteria decision making (MCDM) often faces challenges in dealing with data with different scales and units, as well as ensuring the stability of ranking results against changes in criterion weights. This article proposes a new framework called multi-objective hybrid normalization for ideal solution-based alternative ranking (MONAS) that integrates hybrid normalization techniques and ideal solution approaches to improve the accuracy and consistency of alternative rankings. Weight uncertainty in MCDM reflects the variability or ambiguity in determining the relative importance among criteria, which can affect the stability and reliability of the final decision outcomes; however, this condition can be minimized through the MONAS approach, which adaptively integrates multi-objective normalization, thereby balancing the influence among criteria and improving the consistency of ranking results. The normalization hybrid used in this study combines three approaches: min-max normalization, vector normalization, and sum normalization. These are integrated by calculating the average of the results of these three normalization methods for each data value.  
The data domain used in the MONAS method for evaluating supplier performance includes seven alternatives and six assessment criteria. These criteria represent a combination of benefit and cost attributes, with value distributions that vary across them. Experimental results show that MONAS is capable of producing more reliable rankings that are resilient to data uncertainty compared to conventional methods. Testing results using 10 popular MCDM methods indicate that MONAS can provide more stable and reliable alternative rankings compared to those conventional methods. This framework offers effective and adaptive solutions for various complex decision-making applications across different domains, thereby improving the quality and trust in the decision-making process.