The paper presents a study on the optimization of the Weighted Aggregated Sum Product Assessment (WASPAS) method for multi-criteria decision making. The authors propose a new method that combines the Weighted Sum Model (WSM) and Weighted Product Model (WPM) to improve the accuracy of ranking alternatives. The WASPAS method is defined as a weighted combination of the WSM and WPM results, with the weight parameter λ varying between 0 and 1. The optimal value of λ is determined based on the variance of the relative importance of alternatives, aiming to minimize the dispersion and maximize the accuracy of the estimation. The study analyzes the accuracy of the WSM, WPM, and WASPAS methods, showing that the WASPAS method provides higher accuracy compared to the individual methods. The authors also develop a methodology for evaluating the accuracy of decision-making methods based on initial criteria values. This methodology includes calculating the variance of the relative importance of alternatives and optimizing the λ parameter to achieve the highest accuracy. The paper presents an example of applying the proposed methodology to a multi-criteria decision-making problem involving four alternatives and twelve decision criteria. The results show that the optimal λ values can vary depending on the ratio of the variances of the WSM and WPM results. The ranking of alternatives is performed using the estimated optimal λ values, and the confidence intervals for the relative importance of alternatives are calculated. The study concludes that the proposed WASPAS method improves the accuracy of multi-criteria decision making compared to the WSM and WPM methods. The methodology for optimizing the weighted aggregated function is proposed, enabling the highest accuracy of estimation. The results demonstrate that the WASPAS method is effective in enhancing the ranking accuracy of alternatives in multi-criteria decision support systems.The paper presents a study on the optimization of the Weighted Aggregated Sum Product Assessment (WASPAS) method for multi-criteria decision making. The authors propose a new method that combines the Weighted Sum Model (WSM) and Weighted Product Model (WPM) to improve the accuracy of ranking alternatives. The WASPAS method is defined as a weighted combination of the WSM and WPM results, with the weight parameter λ varying between 0 and 1. The optimal value of λ is determined based on the variance of the relative importance of alternatives, aiming to minimize the dispersion and maximize the accuracy of the estimation. The study analyzes the accuracy of the WSM, WPM, and WASPAS methods, showing that the WASPAS method provides higher accuracy compared to the individual methods. The authors also develop a methodology for evaluating the accuracy of decision-making methods based on initial criteria values. This methodology includes calculating the variance of the relative importance of alternatives and optimizing the λ parameter to achieve the highest accuracy. The paper presents an example of applying the proposed methodology to a multi-criteria decision-making problem involving four alternatives and twelve decision criteria. The results show that the optimal λ values can vary depending on the ratio of the variances of the WSM and WPM results. The ranking of alternatives is performed using the estimated optimal λ values, and the confidence intervals for the relative importance of alternatives are calculated. The study concludes that the proposed WASPAS method improves the accuracy of multi-criteria decision making compared to the WSM and WPM methods. The methodology for optimizing the weighted aggregated function is proposed, enabling the highest accuracy of estimation. The results demonstrate that the WASPAS method is effective in enhancing the ranking accuracy of alternatives in multi-criteria decision support systems.