The transition toward Tourism 4.0 has redefined travel planning as a multifaceted optimization challenge, specifically the Personalized Tourist Trip Design Problem (PTTDP). While conventional navigation services offer basic routing, they frequently lack the capacity to integrate multi-objective constraints with interactive, preference-based spatial visualizations. This research addresses this gap by developing an integrated Spatial Decision Support System (SDSS) that merges the Ant Colony Optimization (ACO) metaheuristic with a Web-based Geographic Information System (WebGIS). The study employs a quantitative methodology, using a weighted-sum scalarization technique to harmonize divergent goals: maximizing destination attraction scores while simultaneously reducing travel distance and duration. Based on empirical validation in Berastagi City, North Sumatra, the results reveal that the ACO-WebGIS framework substantially outperforms traditional routing methods, achieving 17.84% reduction in total distance, 17.24% improvement in time efficiency, and 42.85% increase in the number of POIs visited within identical time constraints, all supported by a swift computational latency of only 1.45 seconds. The scientific value of this work lies in the seamless coupling of algorithmic optimization and dynamic spatial mapping, providing a scalable, robust tool for intelligent tourism management that delivers a mathematically sound yet practical solution for modern travelers.
Marpaung et al. (Wed,) studied this question.