Background: Tourist buses play a critical role in group travel, where service quality, safety, comfort, and operational efficiency directly influence customer satisfaction. However, the selection process for tourist buses is often subjective, lacking structured evaluation mechanisms that account for multiple criteria.
Aims: This study proposes a web-based decision support system (DSS) for tourist bus selection using the Simple Additive Weighting (SAW) method, designed to transform qualitative preferences into quantitative rankings.
Methods and Results: The system evaluates nine well-known bus providers based on three key criteria: price, facilities, and brand, each weighted to reflect decision-making priorities. The SAW method was selected for its computational efficiency and ease of implementation; however, its inherent assumption of full compensability between criteria may lead to biased results in complex decisio contexts. To address this, the proposed framework incorporates expert-driven weight assignment and sensitivity analysis, ensuring that critical non-compensatory attributes such as safety are not overshadowed by other criteria. This integration enhances the robustness and reliability of the final rankings, making the system more adaptable to evolving market demands and customer expectations. Testing demonstrated that the DSS successfully ranked alternatives with transparent, data-driven results, with Melody Transport achieving the highest score (0.825) among the evaluated options. The novelty of this research lies in refining the SAW method for a sector-specific application and addressing its compensatory limitations through expert-based adjustments. This approach not only improves decision quality for consumers and tour operators but also establishes a scalable and intelligent framework for future DSS developments in the tourism transportation sector.