The paper by E. K. Zavadskas, Z. Turskis, J. Antucheviciene, and A. Zakarevicius focuses on optimizing the Weighted Aggregated Sum Product Assessment (WASPAS) method for ranking alternatives in multi-criteria decision-making (MCDM). The authors analyze the Weighted Sum Model (WSM) and Weighted Product Model (WPM) to evaluate their accuracy and propose the WASPAS method as a more accurate alternative. The WASPAS method combines elements of both WSM and WPM, using a weighted sum and a weighted product to determine the relative importance of alternatives. The accuracy of WASPAS is assessed based on the initial criteria values, and an optimization method is developed to find the optimal weight \(\lambda\) that maximizes the accuracy. The results show that WASPAS can achieve up to 1.3 times more accurate ranking compared to WPM and up to 1.6 times more accurate compared to WSM. An example application demonstrates the effectiveness of the proposed methodology, showing that WASPAS can provide higher ranking accuracy and confidence intervals for alternatives. The study concludes that the optimized WASPAS method is a valuable tool for improving the accuracy of MCDM decisions.The paper by E. K. Zavadskas, Z. Turskis, J. Antucheviciene, and A. Zakarevicius focuses on optimizing the Weighted Aggregated Sum Product Assessment (WASPAS) method for ranking alternatives in multi-criteria decision-making (MCDM). The authors analyze the Weighted Sum Model (WSM) and Weighted Product Model (WPM) to evaluate their accuracy and propose the WASPAS method as a more accurate alternative. The WASPAS method combines elements of both WSM and WPM, using a weighted sum and a weighted product to determine the relative importance of alternatives. The accuracy of WASPAS is assessed based on the initial criteria values, and an optimization method is developed to find the optimal weight \(\lambda\) that maximizes the accuracy. The results show that WASPAS can achieve up to 1.3 times more accurate ranking compared to WPM and up to 1.6 times more accurate compared to WSM. An example application demonstrates the effectiveness of the proposed methodology, showing that WASPAS can provide higher ranking accuracy and confidence intervals for alternatives. The study concludes that the optimized WASPAS method is a valuable tool for improving the accuracy of MCDM decisions.