Comparison of WASPAS and TOPSIS Methods in Decision Support Systems

Abstrak

Decision Support Systems (DSS) are essential tools in assisting decision-makers to choose the most optimal alternative from a set of options based on multiple criteria. In the field of Multi-Criteria Decision-Making (MCDM), various methods have been developed to enhance the quality and objectivity of decisions. This research focuses on a comparative analysis between two widely used MCDM techniques: the Weighted Aggregated Sum Product Assessment (WASPAS) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The objective of this study is to evaluate the effectiveness, accuracy, and suitability of each method in supporting decision-making processes within a DSS framework. The research adopts a quantitative approach by applying both methods to the same decision-making problem scenario, which involves selecting the best alternative based on a set of weighted criteria. Data were collected through a simulation case study involving predetermined alternatives and criteria relevant to real-world decision contexts, such as supplier selection and project prioritization. Both methods were implemented using Microsoft Excel and Python-based tools to ensure accuracy in calculation and ease of comparison. The results from each method were then analyzed and compared in terms of ranking outcomes, computational complexity, sensitivity to weight variations, and ease of interpretation. Findings show that both WASPAS and TOPSIS produced consistent and logical rankings of alternatives, but each method offers distinct advantages. WASPAS, which integrates both additive and multiplicative aggregation models, demonstrated higher flexibility and robustness in handling variations in weight assignments.