Optimizing Tourist Trip Design for Urban Sustainability

The rapid growth of mass tourism poses significant challenges to visitor experience and places substantial environmental and operational pressures on heritage sites and urban areas. This study examines the optimization of individual tourist trips and evaluates their broader urban sustainability impact, using Perugia, a historical city in central Italy, as a case study. In particular, we propose a recommendation system for tourist itineraries that, once configured with data on points of interest, takes tourist preferences as input and generates an optimized trip. Each trip is produced by solving a specifically formulated combinatorial optimization problem aimed at maximizing tourist satisfaction. We demonstrate that the problem is NP-hard and we propose three heuristic algorithms to address it. While optimizing individual trips, algorithm executions also update relevant city-wide state variables, managing queues at the different points of interest, which are then used to assess cumulative urban impact. Experiments were conducted as simulations across multiple scenarios with varying numbers of tourists and different arrival patterns. The results compared favorably with a baseline method and demonstrated that well-engineered micro-level trip decisions can positively influence city-wide outcomes.