A Heavy Weighted WASPAS Approach and Its Application into Multi-Attribute Choice Problems
Purpose: The purpose of this paper is to propose a novel multi-attribute decision-making (MADM) method, referred to as the Heavy Modified Weighted Aggregated Sum Product Assessment (WASPAS-HM), which introduces significant enhancements to the original cardinal ranking WASPAS method developed in 2012, particularly for screening decision problems involving large sets of alternatives. Design/Methodology/Approach: The proposed WASPAS-HM method incorporates several methodological modifications, including: (1) the ability to handle ordinal, cardinal, or mixed attribute evaluations; (2) the use of meaningful monotonic normalization techniques; (3) heavy aggregation of normalized attribute values; (4) safe screening procedures for identifying a top subset of promising alternatives; and (5) the re-scaling of heavy additive and multiplicative relative importance for the screened alternatives. The method employs heavy weighted average (HWA) and heavy weighted geometric (HWG) aggregation operators in place of the weighted sum model (WSM) and weighted product model (WPM) used in the original WASPAS framework. Findings: The study introduces the heavy modified WASPAS (WASPAS-HM) screening method as an effective tool for solving complex multi-attribute choice problems. The results demonstrate that the method can robustly accommodate different types of evaluation data while improving ranking stability and discrimination power compared to the original WASPAS approach. Practical Implications: The proposed method is particularly suitable for decision-making contexts involving a large number of pre-specified alternatives, where preliminary screening is essential. A worked numerical example is presented to demonstrate the applicability, relevance, and computational accuracy of the WASPAS-HM method. Originality/Value: This research extends the WASPAS methodology by introducing heavy aggregation operators and a structured screening mechanism, thereby enhancing its flexibility and effectiveness in large-scale MADM problems. The WASPAS-HM method represents a valuable contribution to the decision sciences literature by offering a more robust and adaptable ranking and screening framework.