UDOIP combines dual-layer LLM preference extraction with clustering-and-substitution GRASP to produce higher-scoring, constraint-feasible multi-day urban itineraries than LLM-only and adapted solver baselines on two Chinese city datasets.
Solving the team orienteering problem with time windows and mandatory visits by multi-start simulated annealing
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Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planning
UDOIP combines dual-layer LLM preference extraction with clustering-and-substitution GRASP to produce higher-scoring, constraint-feasible multi-day urban itineraries than LLM-only and adapted solver baselines on two Chinese city datasets.