Multi-Objective Optimization of Itinerary Planning Based on Weather Changes and User Preferences

Existing travel planning systems lack user participation in itinerary scoring and apply coarse, binary weather treatment that risks excluding high-quality outdoor attractions under mild precipitation. This paper presents a multi-objective genetic algorithm (GA)-based itinerary planning system that addresses both limitations. The system incorporates a weather-adaptive POI scoring framework mapping nine weather conditions to three strategies, applying intensity-proportional rain penalties and a geographic flexibility bonus scaled by local indoor alternative density. User preferences are encoded via three integer sliders whose normalised values directly set the GA fitness weights for POI quality, traveling efficiency, and preference satisfaction. The system is evaluated on 144 attractions in Macao. Results show that outdoor POI representation decreases proportionally with precipitation intensity across all nine weather conditions and is substantially suppressed under official extreme weather alerts, while itinerary quality is preserved through the flexibility bonus. Slider adjustment experiments confirm that amplifying each weight produces statistically consistent, direction-correct improvements in its target sub-objective without degrading the others. These findings validate the functional independence of the three-objective fitness formulation and demonstrate that graduated weather treatment and direct user weight control together yield a more responsive and robust itinerary planning system.